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Electrocorticogram (ECoG) based Brain-Computer Interfaces (BCI) provide finer spatial resolution and improved signal-to-noise ratio than its noninvasive counterpart, Electroencephalogram (EEG). This remarkable feature allows for processing in higher spectral bands in order to elucidate more spatially localized encoding mechanisms. We propose an automatic, fully data-driven method to extract relevant...
In most Brain-Computer Interfaces (BCI) experimental paradigms based on Motor Imageries (MI), subjects perform continuous motor imagery (CMI), i.e. a repetitive and prolonged intention of movement, for a few seconds. To improve efficiency such as detecting faster a motor imagery, the purpose of this study is to show the difference between a discrete motor imagery (DMI), i.e. a single short MI, and...
A common problem in Brain-Machine Interface (BMI) is the variations in neural signals over time, leading to significant decrease in decoding performance if the decoder is not re-trained. However, frequent re-training is not practical in real use case. In our work, we found that a temporally more robust system may be achieved through the use of wavelet transform in feature extraction. We used wavelet...
Transcranial Magnetic Stimulation (TMS) treatments have been employed in hospitals. Conventional figure-8 coils can only stimulate a highly localized area, and it is difficult to stimulate specific points accurately without an infrared camera system that is large and expensive. To ease patients' burden, we suggest home treatment with a smaller driving circuit and a smaller positioning system that...
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