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In this study, we propose two adaptive frameworks based on evolutionary algorithms for improving the accuracy of motor imagery (MI) task classification. In an MI task, each participant has a different reaction time delay. In this study, the ...

This paper presents a pioneering exploration into the integration of fine-grained human supervision within the autonomous driving domain to enhance system performance. The current advances in End-to-End autonomous driving normally are data-driven and ...

In the last 10 years a major strand of my research within the Essex Brain-Computer Interfaces and Neural Engineering (BCI-NE) laboratory has focused on the idea of combining brain signals (and other physiological and behavioral information) across ...

SCALO is the first distributed brain-computer interface (BCI) consisting of multiple wireless-networked implants placed on different brain regions. SCALO unlocks new treatment options for debilitating neurological disorders and new research into brain-...

Adaptive robots have the potential to support the overloaded healthcare system by helping new stroke survivors learn about their conditions. However, current adaptive robots often fail to maintain users' engagement during interactions. This study ...

In recent years, the electroencephalography (EEG) brain–computer interface (BCI) has been researched in the area of upper‐limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in ...

Brain-computer interfaces (BCIs) are increasingly used to perform simple operations such as a moving a cursor, but have remained of limited use for more complex tasks. In our new approach to BCI, we use brain relevance feedback to control a generative ...

We present a case-study in the development of a"hyperscanning" auditory interface that transforms realtime brainwave-similarity between interacting dyads into music. Our instrument extends reality in face-to-face communication with a musical stream ...

Interventions aimed at promoting a growth mindset in children range from teaching about the brain's ability to change to playing computer games. In this work, we explore a novel approach to foster a growth mindset by means of interaction with a "magic ...

This paper introduces brainsourcing: utilizing brain responses of a group of human contributors each performing a recognition task to determine classes of stimuli. We investigate to what extent it is possible to infer reliable class labels using data ...

This paper focuses on a framework that uses a small number of features to obtain high-quality classification accuracy of left/right-hand movement motor-imagery EEG signal. Motor-imagery EEG signal has been filtered, and suitable features are extracted ...

In this demonstration we present a prototype for an avatar-mediated social interaction interface that supports the replication of head- and eye movement in distributed virtual environments. In addition to the retargeting of these natural behaviors, the ...

Contemporary Head-Mounted Displays (HMDs) are progressively becoming socially acceptable by approaching the size and design of normal eyewear. Apart from the exciting interaction design prospects, HMDs bear significant potential in hosting an array of ...

While working, we are not always in line with our ability to focus. Being more mindful of own cognitive abilities, including the ability to focus, may contribute to a more pleasant work experience. This paper presents calmworklight, an initial design ...

Millions of children have challenges with anxiety that negatively impact their development, education and well-being. To address this challenge, we developed version 2.0 of Mind-Full, a wearable, mobile neurofeedback system, designed to teach young ...

A method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete ...

This study presents a P300‐based brain‐computer interface (BCI) for mechatronic device driving, i.e. without the need for any physical control. The technique is based on a machine learning (ML) algorithm, which exploits a spatio‐temporal characterization ...

Music-based therapies are increasingly being used to support children with autism with promising clinical results. However, we have a little understanding of which are the most appropriate sounds to be used in music-based interventions for children with ...

Estimating mental workload from brain signals such as Electroencephalography (EEG) has proven very promising in multiple Human-Computer Interaction (HCI) applications, e.g., to design games or educational applications with adaptive difficulty, or to ...

Artists have been using BCIs for artistic expression since the 1960s. Their interest and creativity is now increasing because of the availability of affordable BCI devices and software that does not require them to invest extensive time in getting the ...