FPGA Laboratory, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Sci Rep. 2024 Oct 7;14(1):23291. doi: 10.1038/s41598-024-74681-1.
In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm, and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.
在脑机接口（BMI）领域，将运动意图转化为机器指令的过程被称为解码。然而，尽管最近取得了进展，但解码仍然是 BMI 中的一个巨大挑战。目前在 BMI 领域中使用的解码算法通常涉及计算复杂性，并且需要使用计算机。这主要是因为依赖数学模型来解决解码问题并执行后续输出计算。不幸的是，由于计算机的大小和功耗，它们不适用于植入式 BMI 系统。为了解决这个困境，本研究提出了一种受超维计算启发的开创性方法。该方法首先通过考虑每个神经元的正常发射率分布来识别每个刺激的模式。随后，将每个输入的新观察到的发射模式与每个神经元在每个时刻检测到的模式进行比较。该算法与超维计算有相似之处，将最相似的模式识别为最终输出。这种方法减少了对数学模型的依赖。通过使用从两只雄性猕猴的额眼区（FEF）获得的真实数据集评估该方法的有效性。输出空间包括八个可能的角度。结果表明，准确率为 51.5%，同时表现出显著的低计算复杂性，仅涉及 2050 个加法器运算符。此外，所提出的算法在现场可编程门阵列（FPGA）上实现，并作为标准 CMOS 180nm 技术中的 ASIC 设计，突出了其在实时植入式 BMI 应用中的适用性。实现仅需要 2.3 Kbytes 的 RAM，占用 2.2mm 的面积，在 1.8V 电源下消耗 9.32µW。因此，所提出的解决方案是一种准确、低计算复杂性、硬件友好和实时的方法。
