OpenNIRScap: An Open-Source, Low-Cost Wearable Near-Infrared Spectroscopy-based Brain Interfacing Cap
Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive, real-time method for monitoring brain activity by measuring hemodynamic responses in the cerebral cortex. However, existing systems are expensive, bulky, and limited to clinical or research environments. This paper introduces OpenNIRScap, an open-source, low-cost, and wearable fNIRS system designed to make real-time brain monitoring more accessible in everyday environments. The device features 24 custom-designed sensor boards with dual-wavelength light emitters and photodiode detectors, a central electrical control unit (ECU) with analog multiplexing, and a real-time data processing pipeline. Bench validation and pilot tests on volunteers have confirmed the ability of the system to capture cognitively evoked hemodynamic responses, supporting its potential as an affordable tool for cognitive monitoring and portable neurotechnology applications. The hardware, software, and graphical user interface have all been open-sourced and made publicly available at the following link: https://github.com/tonykim07/fNIRS.
Code (1)
Similar Papers 제목 키워드 기반
HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices
Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisation. The emergence of LLM agents creates a new opportunity for Perso…
Dreamento: an open-source dream engineering toolbox for sleep EEG wearables
We introduce Dreamento (Dream engineering toolbox), an open-source Python package for dream engineering using sleep electroencephalography (EEG) wearables. Dreamento main functions are (1) real-time recording, monitoring…
EEGElectroencephalogram (EEG)Electromyography (EMG)text-to-speech+1AI Foundation Models for Wearable Movement Data in Mental Health Research
Pretrained foundation models and transformer architectures have driven the success of large language models (LLMs) and other modern AI breakthroughs. However, similar advancements in health data modeling remain limited d…
Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrated into wearable devices, these accelerat…
Collaborative InferenceInertia-1: An Open Exploration of Wearable Motion Foundation Models
Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling principles remain poorly understood. Prior …
Human Activity RecognitionRepresentation Learning