Recent advancements in brain-machine interface (BMI) technology offer groundbreaking solutions for individuals with motor impairments, potentially extending to speech synthesis and handwriting assistance. However, current BMIs rely on cumbersome benchtop setups equipped with resource-intensive computing units, restricting their suitability for daily use. We introduce a miniaturized BMI (MiBMI) system capable of accurate, multiclass neural decoding and high-density sensing in a millimeter-scale silicon footprint, making it suitable for next-generation implantable BMIs. A 512-channel, 31-class neural decoder employs a novel concept of distinctive neural code (DNC) driven by a class saliency model. This facilitates the precise translation of intricate neural activity into handwritten characters using a low-complexity linear discriminant analysis (LDA) classifier. The proposed decoder achieves significant improvements in memory utilization ( ${\sim } 100{\times }$ ) and computational complexity ( ${\sim } 320{\times }$ ) compared to a conventional LDA without DNCs. Moreover, MiBMI enables area-efficient 192-channel neural recording through time-division multiplexing, demonstrating its potential for fully integrated BMIs. Fabricated in a 65-nm CMOS process, the high-channel-count BMI chipset occupies a compact area of 2.46 mm2 and consumes $883~{\mu }$ W. The proposed decoder translated human intracortical neural activity into 31 characters with 91.3% accuracy, significantly enhancing the task complexity compared to previous on-chip BMIs. Furthermore, MiBMI achieved 87% accuracy in decoding the neural responses of a rat to six classes of acoustic stimuli in an in vivo experiment.

中文翻译：

---

一个 2.46 mm2 小型化脑机接口 （MiBMI），支持 31 类脑到文本解码


脑机接口 （BMI） 技术的最新进展为运动障碍者提供了开创性的解决方案，并可能扩展到语音合成和手写辅助。然而，当前的 BMI 依赖于配备资源密集型计算单元的笨重的台式设置，限制了它们对日常使用的适用性。我们推出了一种小型化 BMI （MiBMI） 系统，能够在毫米级硅基底面上实现精确的多类神经解码和高密度传感，使其适用于下一代植入式 BMI。512 通道、31 类神经解码器采用了由类显著性模型驱动的独特神经代码 （DNC） 的新概念。这有助于使用低复杂度线性判别分析 （LDA） 分类器将复杂的神经活动精确转换为手写字符。与没有 DNC 的传统 LDA 相比，所提出的解码器在内存利用率 （ ${\sim } 100{\times }$ ） 和计算复杂性 （ ${\sim } 320{\times }$ ） 方面实现了显着改进。此外，MiBMI 通过时分多路复用实现区域高效的 192 通道神经记录，展示了其完全集成 BMI 的潜力。高通道数 BMI 芯片组采用 65 纳米 CMOS 工艺制造，占地面积仅为 2.46 mm2，功耗为 883~{\mu }$ W。所提出的解码器以 91.3% 的准确率将人类皮层内神经活动转换为 31 个字符，与以前的片上 BMI 相比，显着提高了任务复杂性。此外，在体内实验中，MiBMI 在解码大鼠对六类声学刺激的神经反应方面达到了 87% 的准确率。