Brain-Actuated Wireless Mobile Robot Control Through an Adaptive Human–Machine Interface

Electroencephalogram (EEG) signal generated by the brain’s spontaneous recording of electrical activity is often utilized in diagnosis for brain disorders and also employed in rehabilitation devices compared to other biosignals. This work proposes an EEG-based wireless mobile robot control using brain–computer interface (BCI) for people with motor disabilities can interact with robotic systems. An experimental model of mobile robot is explored and it can be controlled by human eye blink strength and attention level. A closed neuro-feedback loop is used to control different mental fatigue. Here, the EEG signals are acquired from neurosky mindwave sensor (single channel prototype) and features of these signals are extracted by adopting discrete wavelet transform (DWT) to enhance signal resolution. Preprocessed signals are impart to robot module, different movements are detected such as right, left, forward, backward, stop positioned on eye blink strength. The proposed adaptive human–machine interface system provides better accuracy and navigates the mobile robot based on user command, so it can be adaptable for disabled people.