Sadasivan Puthusserypady

TL;DR: The results show that EEG to become less complex relative to the normal state with a confidence level of more than 85% due to stimulation, which suggests that when the subjects are under sound or reflexologic stimuli, the number of parallel functional processes active in the brain is less and the brain goes to a more relaxed state.

TL;DR: Compared to the state-of-the-art models evaluated on standard benchmark ECG datasets, the proposed model produced better performance in detecting AF, since the model learns features directly from the data, it avoids the need for clever/cumbersome feature engineering.

TL;DR: Given that the model can learn features from data without having to use specialized feature extraction methods, DL should be considered as an alternative to established EEG classification methods, if enough data is available.

TL;DR: In this paper, an asynchronous brain-computer interface (BCI) system combining the P300 and steady-state visually evoked potentials (SSVEPs) paradigms is proposed.

TL;DR: In this article, a computer-implemented method of providing an interface between a user and a processing unit is proposed, which consists of presenting one or more stimuli to a user, each stimulus varying at a respective stimulation frequency, each stimulation frequency being associated with a respective user-selectable input; receiving at least one signal indicative of brain activity of the user; and determining, from the received signal, which of the stimuli the user attends to and selecting the user selectable input associated with the stimulation frequency of the determined stimuli as being a userselected input.