Biology subjects

McDermott, E. J.

Publications and source records attributed to McDermott, E. J..

3 recordsLinked to original sources

bioRxiv2021

Artifacts in EEG-based BCI therapies: friend or foe?

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain-states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

neuroscience↗

bioRxiv2021

Real-time decoding of 5 finger movements from 2 EMG channels for mixed reality human-computer interaction

The search for optimized forms of human-computer interaction (HCI) has intensified alongside the growing potential for the combination of biosignals with virtual reality (VR) and augmented reality (AR) to enable the next generation of personal computing. At the core, this requires decoding the users biosignals into digital commands. Electromyography (EMG) is a biosensor of particular interest due to the ease of data collection, the relatively high signal-to-noise-ratio, its non-invasiveness, and the ability to interpret the signal as being generated by (intentional) muscle activity. Here, we investigate the potential of using data taken from a simple 2-channel EMG setup to differentiate 5 distinct movements. In particular, EMG was recorded from two bipolar sensors over small hand muscles (extensor digitorum, flexor digitorum profundus) while a subject performed 50 trials of dorsal extension and return for each of the five digits. The maximum and the mean values across the trial were determined for each channel and used as features. A k-nearest neighbors (kNN) classification was performed and overall 5-class classification accuracy reached 94% when using the full trials time window, while simulated real-time classification reached 90.4% accuracy when using the constructed kNN model (k=3) with a 280ms sliding window. Additionally, unsupervised learning was performed and a homogeneity of 85% was achieved. This study demonstrates that reliable decoding of different natural movements is possible with fewer than one channel per class, even without taking into account temporal features of the signal. The technical feasibility of this approach in a real-time setting was validated by sending real-time EMG data to a custom Unity3D VR application through a Lab Streaming Layer to control a user interface. Further use-cases of gamification and rehabilitation were also examined alongside the integration of eye-tracking and gesture recognition for a sensor fusion approach to HCI and user intent.

neuroscience↗

bioRxiv2018

Effects of weight and height on hand selection: a low-cost virtual reality paradigm

The main objective was to evaluate the ability of a virtual reality (VR) system to reliably detect the so-called switch-point of a user; the distinguishing plane between free-choice use of the left and right hand. Independent variables of height and weight were incorporated into the study design and their effects on hand selection were analyzed. The paradigm utilized the Leap Motion Hand Tracker, along with a custom script written in C# and was realized through a Unity3D application. Stimuli appeared in random locations on the computer screen, and required the participant to reach with the hand of their choice to contact them with a virtual hand inside the virtual space. We observed main effects of height and weight on switch-points across the group. We found increased use of the dominant hand as stimuli height increased, as well as a significant increase in overall use of the dominant hand when a 500 gram weighted glove was worn by the non-dominant hand. We validated the average switch-points in VR as compared to real-world setups in previous studies. Our results are in line with previously published real-world data, supporting the use of this paradigm in future VR experiments and applications.

bioengineering↗