Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.

Introduction: As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities.

Methods: This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones.