Abstract
Recent advancements in brain computer interfaces (BCI) have demonstrated control of robotic systems by mental processes alone. Together with invasive BCI, electroencephalographic (EEG) BCI represent an important direction in the development of BCI systems. In the context of EEG BCI, the processing of EEG data is the key challenge. Unfortunately, advances in that direction have been complicated by a lack of large and uniform datasets that could be used to design and evaluate different data processing approaches. In this work, we release a large set of EEG BCI data collected during the development of a slow cortical potentials-based EEG BCI. The dataset contains 60 h of EEG recordings, 13 participants, 75 recording sessions, 201 individual EEG BCI interaction session-segments, and over 60 000 examples of motor imageries in 4 interaction paradigms. The current dataset presents one of the largest EEG BCI datasets publically available to date.
Highlights
Background & SummaryPatients immobilized due to trauma or other medical conditions suffer from a significant deficit of motor and communication functions
Recent advances in neural prosthetics may offer to improve the condition of such patients by allowing them to regain control of certain motor and communication abilities[1,2,3]
Targeted muscle re-innervations[4] and myoelectric control based on residual muscle activity[5,6,7] offer exciting possibilities for neural prosthetics. Another direction is offered by brain-computer interfaces (BCI) that aim to translate neural activity in the brain into control signals for external devices[8,9,10,11,12,13]
Summary
Background & SummaryPatients immobilized due to trauma or other medical conditions suffer from a significant deficit of motor and communication functions. It includes data from 52 subjects, but only 36 min and 240 samples of EEG imagery per subject, and with only a left-right hand MI interaction paradigm.
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