Feasibility of an ultra-low power digital signal processor platform as a basis for a fully implantable brain-computer interface system.

A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP. The system's signal acquisition fidelity was tested and characterized by acquiring harmonic signals from a function generator. In addition, the BCI decoding performance was tested, first with signals from a function generator, and subsequently using human electroencephalogram (EEG) during eyes opening and closing task. On average, the system spent 322 ms to process and analyze 2 s of data. Crosstalk (<;-65 dB) and harmonic distortion (~1%) were minimal. Timing jitter averaged 49 μs per 1000 ms. The online BCI decoding accuracies were 100% for both function generator and EEG data. These results show that a complex BCI algorithm can be executed on an ULP DSP without compromising performance. This suggests that the proposed hardware platform may be used as a basis for future, fully implantable BCI systems.