Human Ephys

BrainFlow BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors, it provides two APIs: Data Acquisition API to obtain data from BCI boards Signal Processing API which is completely independent and can be used without Data Acquisition API Both of these APIs are uniform for all supported boards, so it allows to write completely board agnostic code.

Attys is an wearable data acquisition device with a special focus on biomedical signals such as heart activity (ECG), muscle activity (EMG) and brain activity (EEG). It’s open firmware, open API and has open source applications on github in C++ and JAVA to encourage people to create their own custom versions for mobile devices, tablets and PC.

BioAmp is a biopotential acquisition device (EEG, ECG, EMG, EOG, etc.) developed in the Prototyping Laboratory at the School of Engineering of the National University of Entre Rios (Argentina). Main features:

BPM Biosignal is a two stage amplifier created mainly for educational purposes. Check their YouTube Channel.

BrainMap expands the accessible DIY projects for brain activity measurements. This is the conclusion project of Patrick Dear and Mark Bunney Jr. at Cornell university where they used infrared leds to measure differences in blood flow at the scalp and map the motor cortex.

OpenBCI is a complete open source EEG system that can be built either on top of an Arduino (8-bit system), or on top of chipKIT (32-bit system), which gives the system more local memory and allows for faster speeds.

The openEEG project aims at describing and putting manuals for building a two channel EEG system for about U$200. More on instructions on how to build one, can be found here.

OpenHardwareExG: is a project that provides both open source hardware and software for the measurement and analysis of different types of biosignals From the project page: About the OpenHardwareExG project Project goals The main goal of the project is to build a device that allows the creation of electrophysiologic signal processing applications.