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Deep Learning for Brain-Computer Interface

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Short Description

A brain-computer interface is a device that enables communication and control without movement. The device aims to recognize the human's intentions from spatiotemporal neural activity typically recorded by a large set of electroencephalogram (EEG) electrodes. What makes it particularly challenging, however, is its susceptibility to errors in the recognition of human intentions. Indeed, the recent success of deep learning networks—based on the artificial neural nets of the past—is finding ever expanding applications suggesting its usage for a highly-accrue brain-computer interface.

The first step of this project is to develop an algorithm based on deep learning for noninvasive brain-computer interfaces to classify EEG signals. The next step focuses on an efficient hardware implementation of such algorithm.

Status: Completed

Fall Semester 2017


Machine Learning
HDL coding


40% Theory
30% Architecture Design
30% Verification


Luca Benini

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Detailed Task Description


Practical Details

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