Neurotechnology’s potential to improve lives is unmatched. Neurotech@Berkeley is committed to building innovative, impactful software and hardware solutions to big problems.
Each year, we participate in our parent organization NeurotechX’s Open Challenge where we are tasked with using EEG data to build a software-based solution to a neuroscience problem. We are currently working on a variety of software based projects ranging from learning tools to music selection. On the hardware front, we’re developing custom, non-invasive neural interfaces: EMG-based wristband, EOG-based eye-tracking headset, and a flexible EEG headset.
Build and control an external limb using EMG (building off of Neurowrist work)
Bridging the gap between silicon and biology - the first collegiate initiative to compute and understand live neural circuits!
Design and build a flexible EEG that is affordable and enables user-defined electrode position and count
Novel few-shot ML architecture for efficient calibration of users for generalized EEG & EMG applications, increasing the generalizability of neurotech applications across users.
Structured packages for generalized SSVEP application + “Choose Your Own Adventure” game with a hands-free SSVEP-based controller
Structured packages for generalized P300 application + P300-based lie detector
Utilize real-time processing EMG signals from the wrist and translate captured features into controls
Answering visual questions using image and word embeddings and recurrent attention algorithms
Built machine learning classifier for EMG signals for operating a 3D printed prosthetic arm
EEG Mind-Control Car
Used EEG tracking with a Muse headset for hands-free vehicle control through blinks and winks
Used EEG signals collected through a Muse headset to generate real-time visual art based on the user's brainstate
Canine fNIRS BCI
Built an fNIRS headset for dogs for tracking oxygenation of their olfactory bulb to detect disease states of their owners
Tunable PCB for EMG/EEG
An EOG headset and application for monitoring productivity during work sessions. Analyzes EOG signals to measure focus, fatigue, and alertness and displays metrics in a dashboard.
EEG Music project on mood classification and playback control