[Application to performance augmentation in high-throughput tasks] The conventional goal for a br... more [Application to performance augmentation in high-throughput tasks] The conventional goal for a brain-computer interface has been to restore, for paralyzed individuals, a seamless interaction with the world. The shared vision in this research area is that one day patients will control a prosthetic device with signals originating directly from their brain. This review provides a new perspective on the brain-computer interface (BCI), by asking instead “How can BCI be used to assist neurologically healthy individuals in specifically demanding tasks?” The limited signal-to-noise ratio (SNR) of noninvasive brain signals suggests that one must tailor the application of BCI to tasks where a small increment in information can make a large difference. High throughput tasks may provide such a scenario, as will be exemplified in this review for one such task: rapid visual target detection. BCI can assist in this task by prioritizing perceived target images. Due to the speeded nature of this and...
Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)
Abstrucf-In this paper we use linear discrimination for learning EEG signatures of object recogni... more Abstrucf-In this paper we use linear discrimination for learning EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (63 electrodes) during the rapid presentation (50-200 msec per image) of ...
First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings.
In this paper we describe a method, using linear discrimination, for detecting single-trial EEG s... more In this paper we describe a method, using linear discrimination, for detecting single-trial EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (87 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Subjects were instructed to release a button when they recognized a target image (an image with a person/people). Trials consisted of 100 images each, with a 50% chance of a single target being in a trial. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Linear discrimination enables the estimation of a forward model and thus allows for an approximate localization of the discriminating activity. Results show multiple loci for discriminating activity (e.g. motor and visual). Using these detected EEG signatures, we show that in many cases we can detect targets more accurately than the overt response (button release) and that such signatures can be used to prioritize images for high-throughput search.
The 2006 IEEE International Joint Conference on Neural Network Proceedings, Jul 16, 2006
Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated... more Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with eventrelated information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a laboratory setting and classifying those ERPs is trivial, such a task presents major challenges in a "real world" setting where the EEG signals are recorded when subjects freely move their eyes and the sensory inputs are continuously, as opposed to discretely presented. Here we demonstrate that with the aid of second-order blind identification (SOBI), a blind source separation (BSS) algorithm: (1) we can extract ERPs from such challenging data sets; (2) we were able to obtain meaningful single-trial ERPs in addition to averaged ERPs; and (3) we were able to estimate the spatial origins of these ERPs. Finally, using backpropagation neural networks as classifiers, we show that these single-trial ERPs from specific brain regions can be used to determine moment-to-moment changes in cognitive processing load during a complex "real world" task.
Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information abou... more Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information about brain function. However, because of the low signal-to-noise and signal-to-inference ratios of these imaging modalities, most EEG and fMRI analysis methods estimate relevant activity through trial or event-locked averaging. However, averaging places a limit on the utility of EEG/fMRI, as it does not permit assessment of inter-trial variability critical for understanding the relationship between neural processing and variation in behavioral responses. Single-trial variability may arise as a result of changes in attention, adaptation, or habituation, as well as changes in the recording environment. Our group has developed single-trial EEG analysis based on linear discrimination [1,2] which enables one to relate response variability across trial/stimulus presentation to the underlying electrophysiological variability [3,4]. In this study, we assess whether EEG acquired simultaneously with fMRI is of high enough quality to allow use of such single-trial techniques.
In this paper, we use single-trial analysis of electroencephalography (EEG) to ascertain the cort... more In this paper, we use single-trial analysis of electroencephalography (EEG) to ascertain the cortical origins of response time variability in a rapid serial visual presentation (RSVP) task. We extract spatial components that maximally discriminate between target and distractor stimulus conditions over specific time windows between stimulus onset and the time of a motor response. We then compute the peak latency of this differential activity on a trial-by-trial basis, and correlate this with response time. We find, for our nine participants, that the majority of the latency is introduced by component activity which begins farfrontally 200 ms prior to the response and proceeds to become parietally distributed near the time of response. This activity is consistent with the hypothesis that cortical networks involved in generating the late positive complexes may be the origins of the observed response time variability in rapid discrimination of visual objects.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
We describe a real-time electroencephalography (EEG)-based brain-computer interface system for tr... more We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003
We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preproce... more We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subject's perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms.
We describe our work using linear discrimination of multi-channel electroencephalography for sing... more We describe our work using linear discrimination of multi-channel electroencephalography for single-trial detection of neural signatures of visual recognition events. We demonstrate the approach as a methodology for relating neural variability to response variability, describing studies for response accuracy and response latency during visual target detection. We then show how the approach can be utilized to construct a novel type of brain-computer interface, which we term cortically-coupled computer vision. In this application, a large database of images is triaged using the detected neural signatures. We show how 'corticaltriaging' improves image search over a strictly behavioral response.
In this paper, we describe a simple set of ''recipes'' for the analysis of high spatial density E... more In this paper, we describe a simple set of ''recipes'' for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and nonneural current sources.
We describe a real-time EEG-based brain-computer interface (BCI) system for triaging imagery pres... more We describe a real-time EEG-based brain-computer interface (BCI) system for triaging imagery presented using rapid serial visual presentation (RSVP). A target image in a sequence of non-target distractor images elicits in the EEG a stereotypical spatio-temporal response, which can be detected. A pattern classifier uses this response to re-prioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target vs. non-target images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50 ms time window. Using this simple classifier allows us to process EEG in real-time. The detection accuracy across five subjects is on average 92%, i.e. in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.
Index Terms— electroencephalography, brain–computer interface , cortically–coupled computer vision, rapid serial visual presentation, image triage.
Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated... more Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with event-related information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a laboratory setting and classifying those ERPs is trivial, such a task presents major challenges in a " real world " setting where the EEG signals are recorded when subjects freely move their eyes and the sensory inputs are continuously, as opposed to discretely presented. Here we demonstrate that with the aid of second-order blind identification (SOBI), a blind source separation (BSS) algorithm: (1) we can extract ERPs from such challenging data sets; (2) we were able to obtain meaningful single-trial ERPs in addition to averaged ERPs; and (3) we were able to estimate the spatial origins of these ERPs. Finally, using back-propagation neural networks as classifiers, we show that these single-trial ERPs from specific brain regions can be used to determine moment-to-moment changes in cognitive processing load during a complex " real world " task.
Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information abou... more Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information about brain function. However, because of the low signal-to-noise and signal-to-inference ratios of these imaging modalities, most EEG and fMRI analysis methods estimate relevant activity through trial or event-locked averaging. However, averaging places a limit on the utility of EEG/fMRI, as it does not permit assessment of inter-trial variability critical for understanding the relationship between neural processing and variation in behavioral responses. Single-trial variability may arise as a result of changes in attention, adaptation, or habituation, as well as changes in the recording environment. Our group has developed single-trial EEG analysis based on linear discrimination [1,2] which enables one to relate response variability across trial/stimulus presentation to the underlying electrophysiological variability [3,4]. In this study, we assess whether EEG acquired simultaneously with fMRI is of high enough quality to allow use of such single-trial techniques.
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
Abstract Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity a... more Abstract Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with event-related information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a ...
Application to performance augmentation in high-throughput tasks ] T he conventional goal for a b... more Application to performance augmentation in high-throughput tasks ] T he conventional goal for a brain-computer interface has been to restore, for paralyzed individuals, a seamless interaction with the world. The shared vision in this research area is that one day patients will control a prosthetic device with signals originating directly from their brain. This review provides a new perspective on the brain-computer interface (BCI), by asking instead "How can BCI be used to assist neurologically healthy individuals in specifically demanding tasks?"
2007 3rd International IEEE/EMBS Conference on Neural Engineering, 2007
In this paper we describe a system for simultaneously acquiring EEG and fMRI and evaluate it in t... more In this paper we describe a system for simultaneously acquiring EEG and fMRI and evaluate it in terms of discriminating, single-trial, task-related neural components in the EEG. Using an auditory oddball stimulus paradigm, we acquire EEG data both inside and outside a 1.5T MR scanner and compare both power spectra and single-trial discrimination performance for both conditions. We find that EEG activity acquired inside the MR scanner during echo planer image acquisition is of high enough quality to enable single-trial discrimination performance that is 95% of that acquired outside the scanner. We conclude that EEG acquired simultaneously with fMRI is of high enough fidelity to permit single-trial analysis.
The auditory oddball task is a well-studied stimulus paradigm used to investigate the neural corr... more The auditory oddball task is a well-studied stimulus paradigm used to investigate the neural correlates of simple target detection. It elicits several classic event-related potentials (ERPs), the most prominent being the P300 which is seen as a neural correlate of subjects' detection of rare (target) stimuli. Though trial-averaging is typically used to identify and characterize such ERPs, their latency and amplitude can vary on a trial-to-trial basis reflecting variability in the underlying neural information processing. Here we simultaneously recorded EEG and fMRI during an auditory oddball task and identified cortical areas correlated with the trial-to-trial variability of task-discriminating EEG components. Unique to our approach is a linear multivariate method for identifying taskdiscriminating components within specific stimulus-or response-locked time windows. We find fMRI activations indicative of distinct processes that contribute to the single-trial variability during target detection. These regions are different from those found using standard, including trialaveraged, regressors. Of particular note is strong activation of the lateral occipital complex (LOC). The LOC was not seen when using traditional event-related regressors. Though LOC is typically associated with visual/spatial attention, its activation in an auditory oddball task, where attention can wax and wane from trial-to-trial, indicates it may be part of a more general attention network involved in allocating resources for target detection and decision making. Our results show that trial-to-trial variability in EEG components, acquired simultaneously with fMRI, can yield task-relevant BOLD activations that are otherwise unobservable using traditional fMRI analysis.
In this paper we use single-trial analysis of electroencephalography (EEG) to ascertain the corti... more In this paper we use single-trial analysis of electroencephalography (EEG) to ascertain the cortical origins of response time variability in a rapid serial visual presentation (RSVP) task. We extract spatial components that maximally discriminate between target and distractor stimulus conditions over specific time windows between stimulus onset and the time of a motor response. We then compute the peak latency of this differential activity on a trial-by-trial basis, and correlate this with response time. We find, for our nine participants, that the majority of the latency is introduced by component activity which begins far-frontally 200 ms prior to the response and proceeds to become parietally distributed near the time of response. This activity is consistent with the hypothesis that cortical networks involved in generating the late positive complexes may be the origins of the observed response time variability in rapid discrimination of visual objects.
[Application to performance augmentation in high-throughput tasks] The conventional goal for a br... more [Application to performance augmentation in high-throughput tasks] The conventional goal for a brain-computer interface has been to restore, for paralyzed individuals, a seamless interaction with the world. The shared vision in this research area is that one day patients will control a prosthetic device with signals originating directly from their brain. This review provides a new perspective on the brain-computer interface (BCI), by asking instead “How can BCI be used to assist neurologically healthy individuals in specifically demanding tasks?” The limited signal-to-noise ratio (SNR) of noninvasive brain signals suggests that one must tailor the application of BCI to tasks where a small increment in information can make a large difference. High throughput tasks may provide such a scenario, as will be exemplified in this review for one such task: rapid visual target detection. BCI can assist in this task by prioritizing perceived target images. Due to the speeded nature of this and...
Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)
Abstrucf-In this paper we use linear discrimination for learning EEG signatures of object recogni... more Abstrucf-In this paper we use linear discrimination for learning EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (63 electrodes) during the rapid presentation (50-200 msec per image) of ...
First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings.
In this paper we describe a method, using linear discrimination, for detecting single-trial EEG s... more In this paper we describe a method, using linear discrimination, for detecting single-trial EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (87 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Subjects were instructed to release a button when they recognized a target image (an image with a person/people). Trials consisted of 100 images each, with a 50% chance of a single target being in a trial. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Linear discrimination enables the estimation of a forward model and thus allows for an approximate localization of the discriminating activity. Results show multiple loci for discriminating activity (e.g. motor and visual). Using these detected EEG signatures, we show that in many cases we can detect targets more accurately than the overt response (button release) and that such signatures can be used to prioritize images for high-throughput search.
The 2006 IEEE International Joint Conference on Neural Network Proceedings, Jul 16, 2006
Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated... more Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with eventrelated information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a laboratory setting and classifying those ERPs is trivial, such a task presents major challenges in a "real world" setting where the EEG signals are recorded when subjects freely move their eyes and the sensory inputs are continuously, as opposed to discretely presented. Here we demonstrate that with the aid of second-order blind identification (SOBI), a blind source separation (BSS) algorithm: (1) we can extract ERPs from such challenging data sets; (2) we were able to obtain meaningful single-trial ERPs in addition to averaged ERPs; and (3) we were able to estimate the spatial origins of these ERPs. Finally, using backpropagation neural networks as classifiers, we show that these single-trial ERPs from specific brain regions can be used to determine moment-to-moment changes in cognitive processing load during a complex "real world" task.
Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information abou... more Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information about brain function. However, because of the low signal-to-noise and signal-to-inference ratios of these imaging modalities, most EEG and fMRI analysis methods estimate relevant activity through trial or event-locked averaging. However, averaging places a limit on the utility of EEG/fMRI, as it does not permit assessment of inter-trial variability critical for understanding the relationship between neural processing and variation in behavioral responses. Single-trial variability may arise as a result of changes in attention, adaptation, or habituation, as well as changes in the recording environment. Our group has developed single-trial EEG analysis based on linear discrimination [1,2] which enables one to relate response variability across trial/stimulus presentation to the underlying electrophysiological variability [3,4]. In this study, we assess whether EEG acquired simultaneously with fMRI is of high enough quality to allow use of such single-trial techniques.
In this paper, we use single-trial analysis of electroencephalography (EEG) to ascertain the cort... more In this paper, we use single-trial analysis of electroencephalography (EEG) to ascertain the cortical origins of response time variability in a rapid serial visual presentation (RSVP) task. We extract spatial components that maximally discriminate between target and distractor stimulus conditions over specific time windows between stimulus onset and the time of a motor response. We then compute the peak latency of this differential activity on a trial-by-trial basis, and correlate this with response time. We find, for our nine participants, that the majority of the latency is introduced by component activity which begins farfrontally 200 ms prior to the response and proceeds to become parietally distributed near the time of response. This activity is consistent with the hypothesis that cortical networks involved in generating the late positive complexes may be the origins of the observed response time variability in rapid discrimination of visual objects.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
We describe a real-time electroencephalography (EEG)-based brain-computer interface system for tr... more We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003
We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preproce... more We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subject's perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms.
We describe our work using linear discrimination of multi-channel electroencephalography for sing... more We describe our work using linear discrimination of multi-channel electroencephalography for single-trial detection of neural signatures of visual recognition events. We demonstrate the approach as a methodology for relating neural variability to response variability, describing studies for response accuracy and response latency during visual target detection. We then show how the approach can be utilized to construct a novel type of brain-computer interface, which we term cortically-coupled computer vision. In this application, a large database of images is triaged using the detected neural signatures. We show how 'corticaltriaging' improves image search over a strictly behavioral response.
In this paper, we describe a simple set of ''recipes'' for the analysis of high spatial density E... more In this paper, we describe a simple set of ''recipes'' for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and nonneural current sources.
We describe a real-time EEG-based brain-computer interface (BCI) system for triaging imagery pres... more We describe a real-time EEG-based brain-computer interface (BCI) system for triaging imagery presented using rapid serial visual presentation (RSVP). A target image in a sequence of non-target distractor images elicits in the EEG a stereotypical spatio-temporal response, which can be detected. A pattern classifier uses this response to re-prioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target vs. non-target images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50 ms time window. Using this simple classifier allows us to process EEG in real-time. The detection accuracy across five subjects is on average 92%, i.e. in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.
Index Terms— electroencephalography, brain–computer interface , cortically–coupled computer vision, rapid serial visual presentation, image triage.
Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated... more Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with event-related information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a laboratory setting and classifying those ERPs is trivial, such a task presents major challenges in a " real world " setting where the EEG signals are recorded when subjects freely move their eyes and the sensory inputs are continuously, as opposed to discretely presented. Here we demonstrate that with the aid of second-order blind identification (SOBI), a blind source separation (BSS) algorithm: (1) we can extract ERPs from such challenging data sets; (2) we were able to obtain meaningful single-trial ERPs in addition to averaged ERPs; and (3) we were able to estimate the spatial origins of these ERPs. Finally, using back-propagation neural networks as classifiers, we show that these single-trial ERPs from specific brain regions can be used to determine moment-to-moment changes in cognitive processing load during a complex " real world " task.
Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information abou... more Simultaneous EEG/fMRI has the potential to yield high resolution spatio-temporal information about brain function. However, because of the low signal-to-noise and signal-to-inference ratios of these imaging modalities, most EEG and fMRI analysis methods estimate relevant activity through trial or event-locked averaging. However, averaging places a limit on the utility of EEG/fMRI, as it does not permit assessment of inter-trial variability critical for understanding the relationship between neural processing and variation in behavioral responses. Single-trial variability may arise as a result of changes in attention, adaptation, or habituation, as well as changes in the recording environment. Our group has developed single-trial EEG analysis based on linear discrimination [1,2] which enables one to relate response variability across trial/stimulus presentation to the underlying electrophysiological variability [3,4]. In this study, we assess whether EEG acquired simultaneously with fMRI is of high enough quality to allow use of such single-trial techniques.
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
Abstract Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity a... more Abstract Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with event-related information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a ...
Application to performance augmentation in high-throughput tasks ] T he conventional goal for a b... more Application to performance augmentation in high-throughput tasks ] T he conventional goal for a brain-computer interface has been to restore, for paralyzed individuals, a seamless interaction with the world. The shared vision in this research area is that one day patients will control a prosthetic device with signals originating directly from their brain. This review provides a new perspective on the brain-computer interface (BCI), by asking instead "How can BCI be used to assist neurologically healthy individuals in specifically demanding tasks?"
2007 3rd International IEEE/EMBS Conference on Neural Engineering, 2007
In this paper we describe a system for simultaneously acquiring EEG and fMRI and evaluate it in t... more In this paper we describe a system for simultaneously acquiring EEG and fMRI and evaluate it in terms of discriminating, single-trial, task-related neural components in the EEG. Using an auditory oddball stimulus paradigm, we acquire EEG data both inside and outside a 1.5T MR scanner and compare both power spectra and single-trial discrimination performance for both conditions. We find that EEG activity acquired inside the MR scanner during echo planer image acquisition is of high enough quality to enable single-trial discrimination performance that is 95% of that acquired outside the scanner. We conclude that EEG acquired simultaneously with fMRI is of high enough fidelity to permit single-trial analysis.
The auditory oddball task is a well-studied stimulus paradigm used to investigate the neural corr... more The auditory oddball task is a well-studied stimulus paradigm used to investigate the neural correlates of simple target detection. It elicits several classic event-related potentials (ERPs), the most prominent being the P300 which is seen as a neural correlate of subjects' detection of rare (target) stimuli. Though trial-averaging is typically used to identify and characterize such ERPs, their latency and amplitude can vary on a trial-to-trial basis reflecting variability in the underlying neural information processing. Here we simultaneously recorded EEG and fMRI during an auditory oddball task and identified cortical areas correlated with the trial-to-trial variability of task-discriminating EEG components. Unique to our approach is a linear multivariate method for identifying taskdiscriminating components within specific stimulus-or response-locked time windows. We find fMRI activations indicative of distinct processes that contribute to the single-trial variability during target detection. These regions are different from those found using standard, including trialaveraged, regressors. Of particular note is strong activation of the lateral occipital complex (LOC). The LOC was not seen when using traditional event-related regressors. Though LOC is typically associated with visual/spatial attention, its activation in an auditory oddball task, where attention can wax and wane from trial-to-trial, indicates it may be part of a more general attention network involved in allocating resources for target detection and decision making. Our results show that trial-to-trial variability in EEG components, acquired simultaneously with fMRI, can yield task-relevant BOLD activations that are otherwise unobservable using traditional fMRI analysis.
In this paper we use single-trial analysis of electroencephalography (EEG) to ascertain the corti... more In this paper we use single-trial analysis of electroencephalography (EEG) to ascertain the cortical origins of response time variability in a rapid serial visual presentation (RSVP) task. We extract spatial components that maximally discriminate between target and distractor stimulus conditions over specific time windows between stimulus onset and the time of a motor response. We then compute the peak latency of this differential activity on a trial-by-trial basis, and correlate this with response time. We find, for our nine participants, that the majority of the latency is introduced by component activity which begins far-frontally 200 ms prior to the response and proceeds to become parietally distributed near the time of response. This activity is consistent with the hypothesis that cortical networks involved in generating the late positive complexes may be the origins of the observed response time variability in rapid discrimination of visual objects.
Uploads
Papers by Adam D Gerson
Index Terms— electroencephalography, brain–computer interface , cortically–coupled computer vision, rapid serial visual presentation, image triage.
Index Terms— electroencephalography, brain–computer interface , cortically–coupled computer vision, rapid serial visual presentation, image triage.