Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2018Brain-Computer Interface (BCI) technology translates brain signals into messages. BCI users are thus enabled to interact with the environment by thought, or more generally speaking by mental processes. Event-related desynchronization (ERD) based BCIs use the detection of changes in the spontaneous electroencephalogram (EEG) signal. Different mental processes induce power decreases (ERD) or increases (event-related synchronization, ERS) in different frequencies and different areas of the brain. These differences can be measured and classified. Operating a non-invasive EEG based sensorimotor rhythm BCI is a skill that typically requires extensive training. Lately, online co-adaptive feedback training approaches achieved promising results after short periods of training. Does this also mean that users can have meaningful BCI-based interactions after training, when the BCI is no longer adapting, like in a real- life scenario? To answer this question an online study was conducted with 20 naïve (first time) users. After a short (less than 20 minutes) setup, the users trained to gain BCI control by playing a Whack- A-Mole game where they would have to perform Motor Imagery (imagination of a specific movement- MI) to control a hammer to hit a mole. The game was played for about 30 minutes. During this time, the user learns to perform MI with online feedback from the game and the BCI parameters recurrently adapt to the user’s EEG patterns every~1minute. This recurrent adaptation allows different users to use slightly different strategies and produce ERDs in different frequencies and brain areas without loss of performance. After 30 minutes of training the adaptation was stopped and the users continued playing the game with the trained BCI for another 20 minutes. The BCI parameters were calibrated with data from the adaptive stage and kept fixed in the last 20 minutes. Our hypothesis is that once a system was co-adaptively trained it can maintain its performance without recurrent adaptation. Eighteen out of the twenty users were able to control the BCI and play the game. Seventeen out of the eighteen were able to improve or keep performance between adaptive and non-adaptive stage. These results seem to suggest that online co-adaptation is an effective way to gain BCI control
