A Deep Neural Network for SSVEP-based Brain Computer Interfaces

11/17/2020
by   Osman Berke Guney, et al.
11

The target identification in brain-computer interface (BCI) speller systems refers to the multi-channel electroencephalogram (EEG) classification for predicting the target character that the user intends to spell. The EEG in such systems is known to include the steady-state visually evoked potentials (SSVEP) signal, which is the brain response when the user concentrates on the target while being visually presented a matrix of certain alphanumeric each of which flickers at a unique frequency. The SSVEP in this setting is characteristically dominated at varying degrees by the harmonics of the stimulation frequency; hence, a pattern analysis of the SSVEP can solve for the mentioned multi-class classification problem. To this end, we propose a novel deep neural network (DNN) architecture for the target identification in BCI SSVEP spellers. The proposed DNN is an end-to-end system: it receives the multi-channel SSVEP signal, proceeds with convolutions across the sub-bands of the harmonics, channels and time, and classifies at the fully connected layer. Our experiments are on two publicly available (the benchmark and the BETA) datasets consisting of in total 105 subjects with 40 characters. We train in two stages. The first stage obtains a global perspective into the whole SSVEP data by exploiting the commonalities, and transfers the global model to the second stage that fine tunes it down to each subject separately by exploiting the individual statistics. In our extensive comparisons, our DNN is demonstrated to significantly outperform the state-of-the-art on the both two datasets, by achieving the information transfer rates (ITR) 265.23 bits/min and 196.59 bits/min, respectively. To the best of our knowledge, our ITRs are the highest ever reported performance results on these datasets. The code, and the proposed DNN model are available at https://github.com/osmanberke/Deep-SSVEP-BCI.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 7

page 10

09/05/2021

FBCNN: A Deep Neural Network Architecture for Portable and Fast Brain-Computer Interfaces

Objective: To propose a novel deep neural network (DNN) architecture – t...
05/13/2019

Building Brain Invaders: EEG data of an experimental validation

We describe the experimental procedures for a dataset that we have made ...
10/08/2020

Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification

In this work, we used a deep convolutional neural network (DCNN) to clas...
02/07/2022

Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition

This paper tackles the problem of subject adaptive EEG-based visual reco...
06/06/2021

A novel Deep Neural Network architecture for non-linear system identification

We present a novel Deep Neural Network (DNN) architecture for non-linear...
10/09/2021

Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control

We present a novel adaptive deep joint source-channel coding (JSCC) sche...
01/02/2022

DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis

Recent advancements in Electroencephalography (EEG) sensor technologies ...

Code Repositories

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.