Data-driven approach to designing a BCI-integrated smart wheelchair through cost–benefit analysis

A smart wheelchair provides mobility assistance to persons with motor disabilities by processing sensory inputs from the person. This involves accurately collecting inputs from the user during various movement activities and using them to determine their intended motion. These smart wheelchairs work by collecting brain signals in the form of electroencephalography (EEG) signals and by processing them into a quantized format to provide movement assistance to people. Such systems can be referred to as brain–computer interface (BCI) systems that work with EEG signals. Acquiring data from human beings in the form of brain signals through EEG, along with processing of those signals and ensuring the correctness of actions instigated by those brain signals involve a huge amount of data. In this work, we carried out an experiment by taking 100 human subjects and recording their brain signals using a NeuroMax device. Typical wheelchairs are constrained by design as the motion of those is limited either by manual operation or controlled by haptic sensors and actuators. The main objective in this work was to design a wheelchair with better usability and control using machine learning-based knowledge, which is typically a data-driven approach. However, the proposed approach was designed to take inputs from human gestures and brain sensory activities to provide better usability to the wheelchair. The attention meditation cost–benefit analysis (AMCBA) proposed in this paper aims to reduce the risk of inappropriate results and improve performance by considering various cost–benefit parameters. The said classifier aims to improve the quality of emotion recognition by filtering features from EEG signals using methods of feature selection. The operation of the proposed method is described in two steps: in the first step, we assign weights to different channels for the extraction of spatial and temporal information from human behavior. The second step presents the cost–benefit model to improve the accuracy to help in decision-making. Moreover, we tried to assess the performance of the wheelchair for various assumptions and technical specifications. Finally, this study achieves improved performance in the most difficult circumstances to provide a better experience to persons with immobility.