A brain–computer interface (BCI)-based controller bridges the gap between smart wheelchairs and physically impaired persons with severe conditions. This paper presents the design of a hybrid BCI controller with six classifiers using an electroencephalogram (EEG) headset to detect hand motor imagery (MI) and jaw electromyography (EMG) signals. A BCI controller and semi-autonomous system is developed to control a smart wheelchair in conjunction with its semi-autonomous capabilities. For data acquisition, an openvibe system and a commercial grade EEG headset are used. A multiple common spatial pattern (CSP) filter and Linear discriminant analysis (LDA) classifier system is used to process and classify the user's brain activity. To convert the classifier data into a signal that is compatible with the semi-autonomous wheelchair system, a fuzzy logic controller (FLC) is integrated in LabVIEW. Subjects are trained to use the BCI system and the classifier profiles are optimized for each user and the results are analyzed for this study. The openvibe “Replay” script and recorded training data are used to evaluate the performance of the controller scheme. For each subject, positive, negative, and false-positive executions are recorded. During the initial testing phase, the positive rates for subjects were strong, but false-positive rates were too high to be used. Therefore, the design is iterated by changing the rules of the FLC and configuration of the LabVIEW script. The configuration with the best positive rates for turn executions is chosen where the average positive rate for turning is 0.68 for subject 1 and 0.64 for subject 2.