A 3.9mW 200words/min Neural Signal Processor in Speech Decoding for Brain-Machine Interface

📋 论文概要

该论文提出了一种用于脑机接口语音解码的神经信号处理器，实现了3.9mW功耗和200字/分钟的通信速率。解决了现有脑机接口需要外部刺激或通信速率低的问题，实现了自然语音的高效解码。

💡 主要创新点

• 提出了一种低功耗神经信号处理器，支持实时语音解码，功耗仅3.9mW。
• 实现了200字/分钟的高通信速率，接近自然语音速度，提升了脑机接口的实用性。

🏷 关键词

📄 原文摘要

Brain-machine interfaces (BMIs) are a promising technology that can be applied to AR/VR interfaces, neural prostheses, and machine control. Figure 15.1.1 shows BMI systems based on the source of decoded neural activities: visual stimulation, handwriting, and speech [13]. A visual-stimulation-based BMI [1] decodes intended characters by observing flickering targets, but an external stimulus is necessary. A handwriting-based BMI [2] converts attempted handwriting movements to characters, but the communication rate is low for natural speech. A speech-based BMI [3] translates speech attempts into words and this enables communication at a higher rate. In a speech-based BMI, a neural network (NN) infers the probability of each phone (the smallest unit of speech sound) being spoken according to the extracted brainwave features. The most likely sequence of words can be decoded based on the phone probability with a language model by employing beam search.

👥 作者与机构

Tun-Yu Chang, Jeng-Bang Wang, Yu-Hsuan Tsai, Chia-Hsiang Yang

National Taiwan University, Taipei, Taiwan