Abstract:
Brain-Computer Interface (BCI) provides direct interaction between the brain and computing devices without the need for any physical contact, which makes it a useful tool in applications designed for paralytic patients. Power Wheelchair (PW) control through BCI is one of the principal applications for paralytic patients. The practical implementation of brain-controlled PW is still questionable due to the system’s performance issues, the patient’s physical state, and behavioral sensitivity. Over time, patient’s physical and mental state changes which affect the brain signals and could alter the results of the system. So, there is a challenge to establish a system that is accurate, safe, and efficient enough to act in real-time with a greater number of control commands. To increase the adaptability of BCI based PW, a system that not only maps the brain signals to control commands but also provides the contingency mechanism and speed control is presented. The contingency mechanism of the system helps to tackle such stressful situations by switching the control to joystick and in case of a medical emergency as well. Moreover, the system continuously monitors the mental state along with feature extraction from the brain signals. This continuous monitoring of the mental state will trigger the contingency mechanism when needed, which complements an additional layer of safety and makes the system more compliant. The presented system involves user interference only for the brain signal acquisition after that it performs all the tasks on the computer along with the microcontroller smartly on its own. To test the systems' credibility and efficiency; a series of experiments were performed. The results show that the system is reliable and safe enough. The system also shows satisfactorily True Positive Rate (TPR) and False Positive Rate (FPR) with an average time of 7.7 seconds to generate the interpretable brain signal from the user. To monitor the mental state an auditory-based experiment has been designed which allows the system to learn about the personalized mental states of the user. The system predicts the mental state of the user with an average accuracy of 74.34%
