  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.
