Brain Computer/Machine Interfaces (BCI/BMIs) have substantial potential for enhancing the lives of disabled individuals by restoring functionalities of missing body parts or allowing paralyzed individuals to regain speech and other motor capabilities. Due to severe health hazards arising from skull incisions required for wired BCI/BMIs, scientists are focusing on developing VLSI wireless BCI implants using biomaterials. However, significant challenges, like power efficiency and implant size, persist in creating reliable and efficient wireless BCI implants. With advanced spike sorting techniques, VLSI wireless BCI implants can function within the power and size constraints while maintaining neural spike classification accuracy. This study explores advanced spike sorting techniques to overcome these hurdles and enable VLSI wireless BCI/BMI implants to transmit data efficiently and achieve high accuracy.

翻译：脑机接口/脑机接口（BCI/BMI）在改善残障人士生活质量方面具有巨大潜力，可通过恢复缺失身体部位的功能或使瘫痪患者重新获得语言及其他运动能力来实现。由于有线BCI/BMI所需的颅骨切开术会带来严重健康风险，科学家们正致力于开发基于生物材料的VLSI无线BCI植入体。然而，在构建可靠高效的无线BCI植入体方面，仍存在功耗效率与植入体尺寸等重大挑战。借助高级尖峰分类技术，VLSI无线BCI植入体可在满足功耗与尺寸约束的同时维持神经尖峰分类精度。本研究系统探讨了克服这些障碍的高级尖峰分类方法，旨在使VLSI无线BCI/BMI植入体能够高效传输数据并实现高精度。