361 research outputs found
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables
people to communicate with the outside world by interpreting the EEG signals of
their brains to interact with devices such as wheelchairs and intelligent
robots. More specifically, motor imagery EEG (MI-EEG), which reflects a
subjects active intent, is attracting increasing attention for a variety of BCI
applications. Accurate classification of MI-EEG signals while essential for
effective operation of BCI systems, is challenging due to the significant noise
inherent in the signals and the lack of informative correlation between the
signals and brain activities. In this paper, we propose a novel deep neural
network based learning framework that affords perceptive insights into the
relationship between the MI-EEG data and brain activities. We design a joint
convolutional recurrent neural network that simultaneously learns robust
high-level feature presentations through low-dimensional dense embeddings from
raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various
artifacts such as background activities. The proposed approach has been
evaluated extensively on a large- scale public MI-EEG dataset and a limited but
easy-to-deploy dataset collected in our lab. The results show that our approach
outperforms a series of baselines and the competitive state-of-the- art
methods, yielding a classification accuracy of 95.53%. The applicability of our
proposed approach is further demonstrated with a practical BCI system for
typing.Comment: 10 page
Evaluation of a congruent auditory feedback for Motor Imagery BCI
International audienceDesigning a feedback that helps participants to achieve higher performances is an important concern in brain-computer interface (BCI) research. In a pilot study, we demonstrate how a congruent auditory feedback could improve classification in a electroencephalography (EEG) motor imagery BCI. This is a promising result for creating alternate feedback modality
Klasifikasi Sinyal EEG pada Sistem BCI Pergerakan Jari Manusia Menggunakan Convolutional Neural Network
Pemanfaatan sistem Brain-Computer Interface (BCI) sebagai penghubung pikiran manusia dengan peralatan eksternal sangat bergantung pada keakuratan pengklasifikasian dan pengidentifikasian sinyal EEG khususnya gerak motor imagery. Kesuksesan deep learning, sebagai contoh Convolutional Neural Network (CNN), dalam proses klasifikasi pada berbagai bidang berpeluang untuk diimplementasikan pada klasifikasi gerak motor imagery. Pengimplementasian CNN untuk klasifikasi sinyal EEG motor imagery (MI-EEG) gerakan jari tangan diperkenalkan dalam tulisan ini. Rancangan sistem klasifikasi terdiri dari dua bagian yaitu convolution layer dan multilayer perceptron yang diimplementasikan menggunakan Python 3.7 dengan library TensorFlow 2.0 (Keras). Pengujian rancangan sistem dilakukan terhadap lima subjek dari data MI-EEG 5F dengan frekuensi pencuplikan 200 Hz. Pengujian melibatkan Kfold-cross validation dan analisis pada confusion matrix. Berdasarkan hasil pengujian, peningkatan ukuran kernel menghasilkan peningkatan rata-rata akurasi sistem. Sistem dengan akurasi terbaik diperoleh pada rancangan dengan jumlah kernel 50 sebesar 51,711%. Rancangan sistem menghasilkan kinerja yang melebihi hasil penelitian yang menjadi rujukan utama
The Impact of Context on EEG Motor Imagery Neurofeedback and Related Motor Domains
Neurofeedback (NF) is a versatile non-invasive neuromodulation technique. In combination with motor imagery (MI), NF has considerable potential for enhancing motor performance or supplementing motor rehabilitation. However, not all users achieve reliable NF control. While research has focused on various brain signal properties and the optimisation of signal processing to solve this issue, the impact of context, i.e. the conditions in which NF motor tasks occur, is comparatively unknown. We review current research on the impact of context on MI NF and related motor domains. We identify long-term factors that act at the level of the individual or of the intervention, and short-term factors, with levels before/after and during a session. The reviewed literature indicates that context plays a significant role. We propose considering context factors as well as within-level and across-level interactions when studying MI NF
Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface
Deep learning has shown promise in decoding brain signals, such as
electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs).
However, the non-stationary characteristics of EEG signals pose challenges for
training neural networks to acquire appropriate knowledge. Inconsistent EEG
signals resulting from these non-stationary characteristics can lead to poor
performance. Therefore, it is crucial to investigate and address sample
inconsistency to ensure robust performance in spontaneous BCIs. In this study,
we introduce the concept of sample dominance as a measure of EEG signal
inconsistency and propose a method to modulate its effect on network training.
We present a two-stage dominance score estimation technique that compensates
for performance degradation caused by sample inconsistencies. Our proposed
method utilizes non-parametric estimation to infer sample inconsistency and
assigns each sample a dominance score. This score is then aggregated with the
loss function during training to modulate the impact of sample inconsistency.
Furthermore, we design a curriculum learning approach that gradually increases
the influence of inconsistent signals during training to improve overall
performance. We evaluate our proposed method using public spontaneous BCI
dataset. The experimental results confirm that our findings highlight the
importance of addressing sample dominance for achieving robust performance in
spontaneous BCIs.Comment: 5 pages, 2 figure
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery
Common spatial pattern (CSP) is a popular feature extraction method for
electroencephalogram (EEG) motor imagery (MI). This study modifies the
conventional CSP algorithm to improve the multi-class MI classification
accuracy and ensure the computation process is efficient. The EEG MI data is
gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a
bandpass filter and a time-frequency analysis are performed for each experiment
trial. Then, the optimal EEG signals for every experiment trials are selected
based on the signal energy for CSP feature extraction. In the end, the
extracted features are classified by three classifiers, linear discriminant
analysis (LDA), na\"ive Bayes (NVB), and support vector machine (SVM), in
parallel for classification accuracy comparison. The experiment results show
the proposed algorithm average computation time is 37.22% less than the FBCSP
(1st winner in the BCI Competition IV) and 4.98% longer than the conventional
CSP method. For the classification rate, the proposed algorithm kappa value
achieved 2nd highest compared with the top 3 winners in BCI Competition IV.Comment: Accepted by 42nd Annual International Conferences of the IEEE
Engineering in Medicine and Biology Society in conjunction with the 43rd
Annual Conference of the Canadian Medical and Biological Engineering Society,
202
Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications
A Strong and Simple Deep Learning Baseline for BCI MI Decoding
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network
for Motor Imagery decoding in BCI. Our main motivation is to propose a very
simple baseline to compare to, using only very standard ingredients from the
literature. We evaluate its performance on four EEG Motor Imagery datasets,
including simulated online setups, and compare it to recent Deep Learning and
Machine Learning approaches. EEG-SimpleConv is at least as good or far more
efficient than other approaches, showing strong knowledge-transfer capabilities
across subjects, at the cost of a low inference time. We advocate that using
off-the-shelf ingredients rather than coming with ad-hoc solutions can
significantly help the adoption of Deep Learning approaches for BCI. We make
the code of the models and the experiments accessible
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