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Brain-machine interfaces (BMIs) are increasingly being used in rehabilitation research to improve the quality of life of clinical populations. Current BMI technology allows us to control, with a high level of accuracy, the positioning of robotic hands in space. We have shown previously that it is possible to decode the dexterous movements of fingers during grasping, from noninvasively recorded electroencephalographic...
Reliable two-way communication with the nervous system is one of the biggest challenges in the field of neural engineering. Many problems have prevented us to achieve this goal mostly involving the mechanical, biological and electrical interface between neural tissue and the man-made electrodes. Significant results have been achieved to interface with the brain directly and the current research is...
In EEG-based motor imagery Brain-Computer interface (BCI), EEG data collected in the calibration phase is used as a subject-specific model to classify the EEG data in the evaluation phase. Previous study has shown the feasibility of calibrating EEG-based BCI from passive movement. This paper investigates the primary sensorimotor area activation from fNIRS on 4 subjects using multimodal NIRS and EEG-based...
This paper reports on a study in which a novel virtual moving sound-based spatial auditory brain-computer interface (BCI) paradigm is developed. Classic auditory BCIs rely on spatially static stimuli, which are often boring and difficult to perceive when subjects have non-uniform spatial hearing perception characteristics. The concept of moving sound proposed and tested in the paper allows for the...
This paper introduced a method for biometric authentication using EEG signals. Especially, we focused on the performance of reproducibility for personal authentication. Four healthy subjects participated in the experiment. EEG was measured from only one bipolar channel (O1A2) during resting state with closed eyes. EEG was also recorded with same protocol from same subjects on different days to verify...
We simulate how a two-class brain-computer interface automatically adapts to post-movement imagery bursts of beta band activity (beta rebound) measured in the electroencephalogram at Cz. We used data from 20 healthy, novice volunteers. By combining an adaptive BCI approach with beta rebound features we hypothesize to attain better performance for more users, higher usability and lower setup time than...
Neurofeedback, the self-regulation of brain signals recorded using Electroencephalogram (EEG), allows Brain-Computer Interface (BCI) users to enhance cognitive as well as motor functions using specific training strategies. Therapeutic effects of neurofeedback (by the induction of neuroplasticity) on treatment of people with neurological disorders such as Attention-Deficit Hyperactive Disorder (ADHD),...
Wireless and wearable EEG device for home based long-term non intrusive diagnosis for therapy applications like ASD, ADHD, Epilepsy and other neurological disorders is crucial. This work presents the NeuroMonitor (rev. 2.0) platform designed to record EEG signals from two (bipolar or referential montage) channels. The device is lightweight 41.8g (with 900mAh battery and 3 electrodes) and miniature,...
It is investigated that unilateral sensory stimulation during the motor imagery period might enhance the sensory motor interaction reflected from enhanced sensorimotor rhythm, and the outcomes might be helpful for those with difficulty in producing discriminative rhythmic pattern for successful BCI practice such as hemiplegic stroke patients, amputees and widely existed ‘BCI-illiteracy’ users. In...
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