Medicine and Health Sciences Commons^™

Estimating Cognitive Workload In An Interactive Virtual Reality Environment Using Eeg, Christoph Tremmel, Christain Herff, Tetsuya Sato, Krzysztof Rechowicz, Yusuke Yamani, Dean J. Krusienski

Electrical & Computer Engineering Faculty Publications

With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG …

Empirical Models Of Scalp-Eeg Responses Using Non-Concurrent Intracranial Responses, Komalpreet Kaur, Jerry J. Shih, Dean J. Krusienski

Electrical & Computer Engineering Faculty Publications

Objective- This study presents inter-subject models of scalp-recorded electroencephalographic (sEEG) event-related potentials (ERPs) using intracranially recorded ERPs from electrocorticography and stereotactic depth electrodes in the hippocampus, generally termed as intracranial EEG (iEEG).

Approach- The participants were six patients with medically-intractable epilepsy that underwent temporary placement of intracranial electrode arrays to localize seizure foci. Participants performed one experimental session using a brain-computer interface matrix spelling paradigm controlled by sEEG prior to the iEEG electrode implantation, and one or more identical sessions controlled by iEEG after implantation. All participants were able to achieve excellent spelling accuracy using sEEG, four of the participants …

Signals From Intraventricular Depth Electrodes Can Control A Brain-Computer Interface, Jerry J. Shih, Dean J. Krusienski

Electrical & Computer Engineering Faculty Publications

A Brain-Computer Interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans have used scalp-recorded electroencephalography (EEG). We have recently demonstrated that signals from intracranial electrocorticography (ECoG) and stereotactic depth electrodes (SDE) in the hippocampus can be used to control a BCI P300 Speller paradigm. We report a case in which stereotactic depth electrodes positioned in the ventricle were able to obtain viable signals for a BCI. Our results demonstrate that event-related potentials from intraventricular electrodes can be used to reliably control the …

Exploration Of Computational Methods For Classification Of Movement Intention During Human Voluntary Movement From Single Trial Eeg, Ou Bai, Peter Lin, Sherry Vorbach, Jiang Li, Steve Furlani, Mark Hallett

Electrical & Computer Engineering Faculty Publications

Objective: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).

Methods: Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis …