Advanced spike sorting approaches in implantable VLSI wireless brain computer interfaces: a survey

TL;DR

This survey reviews advanced spike sorting techniques crucial for developing efficient, reliable, and miniaturized VLSI wireless brain-computer interfaces, addressing key challenges like power consumption and size constraints.

Contribution

It provides a comprehensive overview of recent spike sorting methods tailored for VLSI wireless BCI implants, highlighting innovations that improve accuracy and efficiency.

Findings

Enhanced spike sorting algorithms enable smaller, power-efficient BCI implants.

Advanced techniques improve neural spike classification accuracy.

Survey identifies key challenges and future directions in wireless BCI development.

Abstract

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.