IEEE Journal of Translational Engineering in Health and Medicine, 2014
Many commercially available electroencephalography (EEG) sensors, including conventional wet and ... more Many commercially available electroencephalography (EEG) sensors, including conventional wet and dry sensors, can cause skin irritation and user discomfort owing to the foreign material. The EEG products, especially sensors, highly prioritize the comfort level during devices wear. To overcome these drawbacks for EEG sensors, this paper designs Societe Generale de Surveillance S • A • (SGS)-certified, silicon-based dry-contact EEG sensors (SBDSs) for EEG signal measurements. According to the SGS testing report, SBDSs extract does not irritate skin or induce noncytotoxic effects on L929 cells according to ISO10993-5. The SBDS is also lightweight, flexible, and nonirritating to the skin, as well as capable of easily fitting to scalps without any skin preparation or use of a conductive gel. For forehead and hairy sites, EEG signals can be measured reliably with the designed SBDSs. In particular, for EEG signal measurements at hairy sites, the acicular and flexible design of SBDS can push the hair aside to achieve satisfactory scalp contact, as well as maintain low skin-electrode interface impedance. Results of this paper demonstrate that the proposed sensors perform well in the EEG measurements and are feasible for practical applications.
This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients... more This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients. The current version of the expert system can detect EEG and ECG to identify different types of abnormal cardiac rhythms in real-time and identify patients' acute stress. The proposed system also activates an emergency medical alarm system when problems occur.
Objective. Hyperscanning is an emerging technology that concurrently scans the neural dynamics of... more Objective. Hyperscanning is an emerging technology that concurrently scans the neural dynamics of multiple individuals to study interpersonal interactions. In particular, hyperscanning with electroencephalography (EEG) is increasingly popular owing to its mobility and its ability to allow studying social interactions in naturalistic settings at the millisecond scale. Approach. To align multiple EEG time series with sophisticated event markers in a single time domain, a precise and unified timestamp is required for stream synchronization. This study proposes a clock-synchronized method that uses a custom-made RJ45 cable to coordinate the sampling between wireless EEG amplifiers to prevent incorrect estimation of interbrain connectivity due to asynchronous sampling. In this method, analog-to-digital converters are driven by the same sampling clock. Additionally, two clock-synchronized amplifiers leverage additional radio frequency channels to keep the counter of their receiving dongles updated, which guarantees that binding event markers received by the dongle with the EEG time series have the correct timestamp. Main results. The results of two simulation experiments and one video gaming experiment reveal that the proposed method ensures synchronous sampling in a system with multiple EEG devices, achieving near-zero phase lag and negligible amplitude difference between the signals. Significance. According to all of the signal-similarity metrics, the suggested method is a promising option for wireless EEG hyperscanning and can be utilized to precisely assess the interbrain couplings underlying social-interaction behaviors.
Journal of Medical and Biological Engineering, 2010
An EEG-based smart living environmental control system to auto-adjust the living environment is p... more An EEG-based smart living environmental control system to auto-adjust the living environment is proposed in this study. Many environmental control systems have been proposed to improve human life quality in recent years. However, there is little research focused on environment control by using a human's physiological state directly. Even though some studies have proposed brain computer interface-based (BCI-based) environmental control systems, most of them encountered signal quality decline during long-term physiological monitoring with conventional wet electrodes. Moreover, such BCI-based environmental control systems are actively controlled by users; less close-loop feedback capability can be provided between environment and user for automation. Based on the advance of our technique for BCI and the improvement of micro-electro-mechanical-system-based (MEMS-based) dry electrode sensors, we combined these techniques to demonstrate an auto-adjustable living environment control sy...
Significant advances in neuroscience, sensor technologies, and efficient signal processing algori... more Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of hardware and software that guide the communications between users and systems. This article presents several current neurotechnologies and computational intelligence methods applied to EEG-based BCIs. In the hardware aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. With these advantages, these novel EEG devices can acquire real-time EEG signals for operational workplaces without requiring conductive gel/paste or scalp preparations. As for the software aspect, blind source separation, artificial neural networks, effective connectivity measurements and information fusion techniques are introduced to address the technical issues of artifact removal, rapid event-related potential detection, complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 18, 2016
Potable electroencephalography (EEG) devices have become critical for important research. They ha... more Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG sys...
Handbook of Digital Games and Entertainment Technologies, 2015
Abstract : Brain-computer interface (BCI) technologies, or technologies that use online brain sig... more Abstract : Brain-computer interface (BCI) technologies, or technologies that use online brain signal processing, have a great promise to improve human interactions with computers, their environment, and even other humans. Despite this promise, there are no current serious BCI technologies in widespread use, due to the lack of robustness in BCI technologies. The key neural aspect of this lack of robustness is human variability, which has two main components: (1) individual differences in neural signals and (2) intraindividual variability over time. In order to develop widespread BCI technologies, it will be necessary to address this lack of robustness. However, it is currently unknown how neural variability affects BCI performance. To accomplish these goals, it is essential to obtain data from large numbers of individuals using BCI technologies over considerable lengths of time. One promising method for this is through the use of BCI technologies embedded into games with a purpose (GWAP). GWAP are a game-based form of crowdsourcing which players choose to play for enjoyment and during which the player performs key tasks which cannot be automated but that are required to solve research questions. By embedding BCI paradigms in GWAP and recording neural and behavioral data, it should be possible to much more clearly understand the differences in neural signals between individuals and across different time scales, enabling the development of novel and increasingly robust adaptive BCI algorithms.
2015 International Joint Conference on Neural Networks (IJCNN), 2015
Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many r... more Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments.
The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS '05.
Abstract A novel method has been developed for the manufacture of a three dimensional multi-elect... more Abstract A novel method has been developed for the manufacture of a three dimensional multi-electrode array (3D MEA), particularly, the shape of micro-tips can be varied by MEMS technology to construct different multi-electrode array. It improved the disadvantage of ...
IEEE Transactions on Biomedical Circuits and Systems, 2014
Brain activity associated with attention sustained on the task of safe driving has received consi... more Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.
Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to founda... more Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to foundational insights into how humans sense, perceive, and interact with the external world. However, it is argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. Thus, it remains unclear how well the current knowledge of human brain function translates into the highly dynamic real world. While some demonstrated successes in real-world neurotechnologies are observed, particularly in the area of brain-computer interaction technologies, innovations and developments to date are limited to a small science and technology community. We posit that advancements in realworld neuroimaging tools for use by a broad-based workforce will dramatically enhance neurotechnology applications that have the potential to radically alter human-system interactions across all aspects of everyday life. We discuss the efforts of a joint government-academic-industry team to take an integrative, interdisciplinary, and multi-aspect approach to translate current technologies into devices that are truly fieldable across a range of environments. Results from initial work, described here, show promise for dramatic advances in the field that will rapidly enhance our ability to assess brain activity in real-world scenarios.
In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signa... more In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain
Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, i... more Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.
A multi-electrode array (MEA) with 16 channels was designed to record simultaneously the velocity... more A multi-electrode array (MEA) with 16 channels was designed to record simultaneously the velocity of conduction of neurons in a measurement system for bio-medical applications. MEA were fabricated with MEMS technology on a silicon-on-insulator (SOI) wafer, which controls the thickness of the probe effectively. All used probes have length 3 mm and width 100 m. The thickness of the probe, 25 m, was defined by the thickness of the device layer on the SOI wafer. The multiple probes with a 16-site recording electrode array have been manufactured; their strength was tested with a force gauge and their electrical performance was tested with an impedance measurement system. The readout circuitry comprises an array of 16-site preamplifiers fully integrated on a chip that is capable of signal processing to improve the signal-noise-ratio (SNR). To demonstrate the capability, multiple neural signals were recorded simultaneously with all electrodes from each separate probe. To verify its capability of measuring neural signals, the MEA was used to measure these signals from the electrophysiology system of crayfish. The velocity of neural conduction recorded with a fabricated MEA is shown, and is comparable with a measurement with a traditional glass pipette. The MEA for recording neural signals would be improved in further development.
2014 IEEE International Conference on Consumer Electronics - Taiwan, 2014
This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients... more This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients. The current version of the expert system can detect EEG and ECG to identify different types of abnormal cardiac rhythms in real-time and identify patients' acute stress. The proposed system also activates an emergency medical alarm system when problems occur.
Online artifact rejection, feature extraction, and pattern recognition are essential to advance t... more Online artifact rejection, feature extraction, and pattern recognition are essential to advance the Brain Computer Interface (BCI) technology so as to be practical for real-world applications. The goals of BCI system should be a small size, rugged, lightweight, and ...
IEEE Journal of Translational Engineering in Health and Medicine, 2014
Many commercially available electroencephalography (EEG) sensors, including conventional wet and ... more Many commercially available electroencephalography (EEG) sensors, including conventional wet and dry sensors, can cause skin irritation and user discomfort owing to the foreign material. The EEG products, especially sensors, highly prioritize the comfort level during devices wear. To overcome these drawbacks for EEG sensors, this paper designs Societe Generale de Surveillance S • A • (SGS)-certified, silicon-based dry-contact EEG sensors (SBDSs) for EEG signal measurements. According to the SGS testing report, SBDSs extract does not irritate skin or induce noncytotoxic effects on L929 cells according to ISO10993-5. The SBDS is also lightweight, flexible, and nonirritating to the skin, as well as capable of easily fitting to scalps without any skin preparation or use of a conductive gel. For forehead and hairy sites, EEG signals can be measured reliably with the designed SBDSs. In particular, for EEG signal measurements at hairy sites, the acicular and flexible design of SBDS can push the hair aside to achieve satisfactory scalp contact, as well as maintain low skin-electrode interface impedance. Results of this paper demonstrate that the proposed sensors perform well in the EEG measurements and are feasible for practical applications.
This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients... more This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients. The current version of the expert system can detect EEG and ECG to identify different types of abnormal cardiac rhythms in real-time and identify patients' acute stress. The proposed system also activates an emergency medical alarm system when problems occur.
Objective. Hyperscanning is an emerging technology that concurrently scans the neural dynamics of... more Objective. Hyperscanning is an emerging technology that concurrently scans the neural dynamics of multiple individuals to study interpersonal interactions. In particular, hyperscanning with electroencephalography (EEG) is increasingly popular owing to its mobility and its ability to allow studying social interactions in naturalistic settings at the millisecond scale. Approach. To align multiple EEG time series with sophisticated event markers in a single time domain, a precise and unified timestamp is required for stream synchronization. This study proposes a clock-synchronized method that uses a custom-made RJ45 cable to coordinate the sampling between wireless EEG amplifiers to prevent incorrect estimation of interbrain connectivity due to asynchronous sampling. In this method, analog-to-digital converters are driven by the same sampling clock. Additionally, two clock-synchronized amplifiers leverage additional radio frequency channels to keep the counter of their receiving dongles updated, which guarantees that binding event markers received by the dongle with the EEG time series have the correct timestamp. Main results. The results of two simulation experiments and one video gaming experiment reveal that the proposed method ensures synchronous sampling in a system with multiple EEG devices, achieving near-zero phase lag and negligible amplitude difference between the signals. Significance. According to all of the signal-similarity metrics, the suggested method is a promising option for wireless EEG hyperscanning and can be utilized to precisely assess the interbrain couplings underlying social-interaction behaviors.
Journal of Medical and Biological Engineering, 2010
An EEG-based smart living environmental control system to auto-adjust the living environment is p... more An EEG-based smart living environmental control system to auto-adjust the living environment is proposed in this study. Many environmental control systems have been proposed to improve human life quality in recent years. However, there is little research focused on environment control by using a human's physiological state directly. Even though some studies have proposed brain computer interface-based (BCI-based) environmental control systems, most of them encountered signal quality decline during long-term physiological monitoring with conventional wet electrodes. Moreover, such BCI-based environmental control systems are actively controlled by users; less close-loop feedback capability can be provided between environment and user for automation. Based on the advance of our technique for BCI and the improvement of micro-electro-mechanical-system-based (MEMS-based) dry electrode sensors, we combined these techniques to demonstrate an auto-adjustable living environment control sy...
Significant advances in neuroscience, sensor technologies, and efficient signal processing algori... more Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of hardware and software that guide the communications between users and systems. This article presents several current neurotechnologies and computational intelligence methods applied to EEG-based BCIs. In the hardware aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. With these advantages, these novel EEG devices can acquire real-time EEG signals for operational workplaces without requiring conductive gel/paste or scalp preparations. As for the software aspect, blind source separation, artificial neural networks, effective connectivity measurements and information fusion techniques are introduced to address the technical issues of artifact removal, rapid event-related potential detection, complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 18, 2016
Potable electroencephalography (EEG) devices have become critical for important research. They ha... more Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG sys...
Handbook of Digital Games and Entertainment Technologies, 2015
Abstract : Brain-computer interface (BCI) technologies, or technologies that use online brain sig... more Abstract : Brain-computer interface (BCI) technologies, or technologies that use online brain signal processing, have a great promise to improve human interactions with computers, their environment, and even other humans. Despite this promise, there are no current serious BCI technologies in widespread use, due to the lack of robustness in BCI technologies. The key neural aspect of this lack of robustness is human variability, which has two main components: (1) individual differences in neural signals and (2) intraindividual variability over time. In order to develop widespread BCI technologies, it will be necessary to address this lack of robustness. However, it is currently unknown how neural variability affects BCI performance. To accomplish these goals, it is essential to obtain data from large numbers of individuals using BCI technologies over considerable lengths of time. One promising method for this is through the use of BCI technologies embedded into games with a purpose (GWAP). GWAP are a game-based form of crowdsourcing which players choose to play for enjoyment and during which the player performs key tasks which cannot be automated but that are required to solve research questions. By embedding BCI paradigms in GWAP and recording neural and behavioral data, it should be possible to much more clearly understand the differences in neural signals between individuals and across different time scales, enabling the development of novel and increasingly robust adaptive BCI algorithms.
2015 International Joint Conference on Neural Networks (IJCNN), 2015
Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many r... more Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments.
The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS '05.
Abstract A novel method has been developed for the manufacture of a three dimensional multi-elect... more Abstract A novel method has been developed for the manufacture of a three dimensional multi-electrode array (3D MEA), particularly, the shape of micro-tips can be varied by MEMS technology to construct different multi-electrode array. It improved the disadvantage of ...
IEEE Transactions on Biomedical Circuits and Systems, 2014
Brain activity associated with attention sustained on the task of safe driving has received consi... more Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.
Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to founda... more Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to foundational insights into how humans sense, perceive, and interact with the external world. However, it is argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. Thus, it remains unclear how well the current knowledge of human brain function translates into the highly dynamic real world. While some demonstrated successes in real-world neurotechnologies are observed, particularly in the area of brain-computer interaction technologies, innovations and developments to date are limited to a small science and technology community. We posit that advancements in realworld neuroimaging tools for use by a broad-based workforce will dramatically enhance neurotechnology applications that have the potential to radically alter human-system interactions across all aspects of everyday life. We discuss the efforts of a joint government-academic-industry team to take an integrative, interdisciplinary, and multi-aspect approach to translate current technologies into devices that are truly fieldable across a range of environments. Results from initial work, described here, show promise for dramatic advances in the field that will rapidly enhance our ability to assess brain activity in real-world scenarios.
In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signa... more In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain
Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, i... more Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.
A multi-electrode array (MEA) with 16 channels was designed to record simultaneously the velocity... more A multi-electrode array (MEA) with 16 channels was designed to record simultaneously the velocity of conduction of neurons in a measurement system for bio-medical applications. MEA were fabricated with MEMS technology on a silicon-on-insulator (SOI) wafer, which controls the thickness of the probe effectively. All used probes have length 3 mm and width 100 m. The thickness of the probe, 25 m, was defined by the thickness of the device layer on the SOI wafer. The multiple probes with a 16-site recording electrode array have been manufactured; their strength was tested with a force gauge and their electrical performance was tested with an impedance measurement system. The readout circuitry comprises an array of 16-site preamplifiers fully integrated on a chip that is capable of signal processing to improve the signal-noise-ratio (SNR). To demonstrate the capability, multiple neural signals were recorded simultaneously with all electrodes from each separate probe. To verify its capability of measuring neural signals, the MEA was used to measure these signals from the electrophysiology system of crayfish. The velocity of neural conduction recorded with a fabricated MEA is shown, and is comparable with a measurement with a traditional glass pipette. The MEA for recording neural signals would be improved in further development.
2014 IEEE International Conference on Consumer Electronics - Taiwan, 2014
This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients... more This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients. The current version of the expert system can detect EEG and ECG to identify different types of abnormal cardiac rhythms in real-time and identify patients' acute stress. The proposed system also activates an emergency medical alarm system when problems occur.
Online artifact rejection, feature extraction, and pattern recognition are essential to advance t... more Online artifact rejection, feature extraction, and pattern recognition are essential to advance the Brain Computer Interface (BCI) technology so as to be practical for real-world applications. The goals of BCI system should be a small size, rugged, lightweight, and ...
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