Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices without relying on traditional motor pathways or speech. This is achieved by recording and interpreting neural activity using different types of electrodes. Invasive BCIs use microelectrodes implanted directly into the brain to capture high-resolution neural signals, while non-invasive BCIs, like those using electroencephalography (EEG), detect cortical activity through surface electrodes placed on the scalp. BCI applications can be broadly categorized into clinical and non-clinical domains. Clinical applications include assistive technologies (e.g., controlling prosthetic limbs, communication devices for individuals with paralysis) and neurorehabilitation (e.g., stroke recovery through motor imagery training). Non-clinical applications include but are not limited to brain-controlled gaming, robotics, cognitive state monitoring, neurofeedback, and emotion detection.
Our current ongoing projects in EEG-based BCI focus on Surprise Detection and Drowsiness Detection. While driving, a driver may experience surprise due to various factors, such as sudden changes in other drivers’ behavior, unexpected obstacles, etc., which can lead to a loss of concentration. We aim to detect surprise in near real-time by analyzing brain signals captured through EEG. The same applies to drowsiness. If the system detects that the driver is becoming sleepy, it can trigger alarms until the driver is fully alert again. These systems have the potential to enhance road safety and prevent accidents.