Development of Brain-Computer Interface (BCI) model for real- time applications using DSP processors

Brain computer interfaces (BCIs) are systems that provide translation of the electrical activity of the brain into commands which can control devices in real-time. It is known that most BCI based application involves non-invasive EEG signals in combination with machine learning algorithms for control of devices. Although there has been some real- time implementation of BCI including cursor movements controlled by a persons thoughts, they have not been done as a portable device, but rather as a laboratory experiment controlled using multi- core processors and computers. This paper focuses on real- time implementation of BCI for motor imagery tasks using a floating point DSP processor (TMS320C6713DSK). The proposed work involves FIR based preprocessing filter to remove extraneous noise including Electrooculogram (EOG) and Electromyographic (EMG) signals followed by feature extraction using time-frequency domain features. The implications of this kind of a development in Brain- Computer Interface would be tremendous since the whole system can be made into a portable device or a System on a Chip (SoC).