Brain Controlled Switch

This study aims at designing and implementing a single channel stand-alone Brain-Controlled Switch (BCS) device, which records the electroencephalography (EEG) signals from the scalp using electrodes, amplifies it, eliminates interferences (associated with the EEG signals) and processes the EEG signals to extract and decode temporal signal features to determine user’s intention of regulating an external switch. The design of our “brain-controlled switch” device is implemented using a bio-potential amplifier and a microcontroller. The bio-potential amplifier amplifies the EEG signals to a level sufficient for processing, eliminates interferences and ensures patient safety. The microcontroller (dsPIC30F4013) digitizes the amplified and conditioned analog EEG signals from the bio-potential amplifier, extracts the desired signal features for decoding and prediction of user’s intention and accordingly operates the external switch. When the user concentrates on an external visual stimulus or performs externally triggered movement (hand movement or motor imagery movement), a reproducible pattern appears in user’s EEG frequency bands. The analysis of these patterns is used to decode and predict user’s intention to operate an external switch. To realize our “brain-controlled switch”, we explored two EEG sources: steady-state visually evoked potentials (SSVEP) and beta rebounds, which are patterns generated in the EEG frequency bands associated with focusing on an external visual stimulus or performing externally triggered movements. In case of SSVEP based brain controlled switch, a repetitive visual stimulus (LED flickering at a specified frequency) was used. When the user concentrates on the flickering LED, a dominant fundamental frequency (equivalent to the flickering frequency) appears in the spectral representation of the EEG signals recorded at occipital lobes. Our microcontroller implemented a digital band pass filter to extract the frequency band containing this fundamental frequency and continuously took an average of the amplitude power every predetermined time interval. Whenever the amplitude average power exceeded the preset power threshold the external switch was turned ON. A healthy subject participated in this study, and it took approximately 3.14 ± 1.81 seconds of active concentration for the subject to turn ON the switch in real time with a false positive rate of 1.17%. In case of beta rebound based brain controlled switch, the subject was instructed to perform a brisk hand movement following an external synchronization signal. Our design focused on the post-movement beta rebound which occurs after the cessation of the movement to operate the external switch. Our microcontroller in this case implemented a digital band pass filter to extract the beta band and continuously took an average of its amplitude power every predetermined time interval. Whenever the amplitude average power exceeded the preset power threshold the external switch was turned ON. It took approximately 12.23 ± 7.39 seconds of active urging time by the subject to turn ON the switch in real time with a false positive rate of 9.33%. Thus we have designed a novel stand-alone BCS device which operates an external switch by decoding and predicting user’s intentions