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

机译：超低功耗数字信号处理器平台作为完全植入式脑机接口系统的基础的可行性

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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.
机译：完全可植入的脑机接口（BCI）可以成为恢复受脊髓损伤影响者的独立性的实用工具。我们设想这种BCI系统将侵入性地获取脑部信号（例如脑电图）并将其转换为外部假体的控制命令。通过以商用超低功耗（ULP）数字信号处理器（DSP，TMS320C5517，Texas Instruments）为中心实现其台式模拟机，测试了这种系统的可行性。在DSP中实现并测试了一组信号处理和BCI算法，包括（解复用），快速傅立叶变换，功率谱密度，主成分分析，线性判别分析，贝叶斯规则和有限状态机。该系统的信号采集保真度已经过测试，并通过从函数发生器中获取谐波信号进行了表征。此外，首先对来自函数发生器的信号进行了BCI解码性能的测试，然后在睁眼和闭眼的过程中使用了人类脑电图（EEG）。平均而言，系统花费322毫秒来处理和分析2 s的数据。串扰（<-65 dB）和谐波失真（〜1％）最小。定时抖动平均为每1000 ms 49μs。函数生成器和EEG数据的在线BCI解码精度均为100％。这些结果表明，可以在ULP DSP上执行复杂的BCI算法而不会影响性能。这表明，建议的硬件平台可以用作将来的完全可植入BCI系统的基础。

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