A Scalable Real-Time Architecture for Neural Oscillation Detection and Phase-Specific Stimulation

TL;DR

This paper introduces a scalable, real-time, platform-agnostic system architecture for detecting neural oscillations and delivering phase-specific stimulation, enabling advanced brain research and potential clinical applications.

Contribution

It presents a novel scalable architecture for real-time neural oscillation detection and phase-specific stimulation that overcomes limitations of existing systems.

Findings

Validated with a microcontroller-based prototype

Scales to thousands of recording channels

Provides real-time timing guarantees

Abstract

Oscillations in the local field potential (LFP) of the brain are key signatures of neural information processing. Perturbing these oscillations at specific phases in order to alter neural information processing is an area of active research. Existing systems for phase-specific brain stimulation typically either do not offer real-time timing guarantees (desktop computer based systems) or require extensive programming of vendor-specific equipment. This work presents a real-time detection system architecture that is platform-agnostic and that scales to thousands of recording channels, validated using a proof-of-concept microcontroller-based implementation.