Task-Dependent Signal Variations in EEG Error-Related Potentials for Brain-Computer Interfaces
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Brain-computer interfaces, as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. In this paper we exploit a unique feat ...
Brain-computer interfaces (BCIs), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve ...
Recent experiments have shown the possibility to use the brain electrical activity to directly control the movement of robots or prosthetic devices in real time. Such neuroprostheses can be invasive or non-invasive, depending on how the brain signals are r ...
Spatial filtering is a widely used dimension reduction method in electroencephalogram based brain-computer interface systems. In this paper a new algorithm is proposed, which learns spatial filters from a training dataset. In contrast to existing approache ...
Brain imaging studies in man and single cell recordings in monkey have suggested that medial supplementary motor areas (SMA) and lateral pre-motor areas (PMA) are functionally dissociated concerning their involvement in internally driven and externally cue ...
In order to permit a brain computer efficient communication, it is important to dispose of an efficient algorithm to decode the brain electrical activity. We will focus our attention on an algorithm based on microstates segmentation of the brain electrical ...
