  Brain-machine interfaces (BMIs) have emerged as a transformative force in
assistive technologies, empowering individuals with motor impairments by
enabling device control and facilitating functional recovery. However, the
persistent challenge of inter-session variability poses a significant hurdle,
requiring time-consuming calibration at every new use. Compounding this issue,
the low comfort level of current devices further restricts their usage. To
address these challenges, we propose a comprehensive solution that combines a
tiny CNN-based Transfer Learning (TL) approach with a comfortable, wearable EEG
headband. The novel wearable EEG device features soft dry electrodes placed on
the headband and is capable of on-board processing. We acquire multiple
sessions of motor-movement EEG data and achieve up to 96% inter-session
accuracy using TL, greatly reducing the calibration time and improving
usability. By executing the inference on the edge every 100ms, the system is
estimated to achieve 30h of battery life. The comfortable BMI setup with tiny
CNN and TL paves the way to future on-device continual learning, essential for
tackling inter-session variability and improving usability.
