  Brain Computer Interface (BCI) has great potential for solving many brain
signal analysis limitations, mental disorder resolutions, and restoring missing
limb functionality via neural-controlled implants. However, there is no single
available, and safe implant for daily life usage exists yet. Most of the
proposed implants have several implementation issues, such as infection hazards
and heat dissipation, which limits their usability and makes it more
challenging to pass regulations and quality control production. The wireless
implant does not require a chronic wound in the skull. However, the current
complex clustering neuron identification algorithms inside the implant chip
consume a lot of power and bandwidth, causing higher heat dissipation issues
and draining the implant's battery. The spike sorting is the core unit of an
invasive BCI chip, which plays a significant role in power consumption,
accuracy, and area. Therefore, in this study, we propose a low-power adaptive
simplified VLSI architecture, "Zydeco-Style," for BCI spike sorting that is
computationally less complex with higher accuracy that performs up to 93.5% in
the worst-case scenario. The architecture uses a low-power Bluetooth Wireless
communication module with external IoT medical ICU devices. The proposed
architecture was implemented and simulated in Verilog. In addition, we are
proposing an implant conceptual design.
