scholarly journals Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5436
Author(s):  
Kyungho Won ◽  
Moonyoung Kwon ◽  
Minkyu Ahn ◽  
Sung Chan Jun
Keyword(s):  
Motor Imagery ◽  
Comparative Performance ◽  
Computer Interfaces ◽  
Model Generalization ◽  
Improve Model ◽  
Eeg Data ◽  
Subject Variability ◽  
Pooling Strategies ◽  
Pooling Strategy ◽  
Electroencephalogram Eeg

Brain–computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition

2019 ◽  
Vol 31 (5) ◽  
pp. 919-942 ◽  
Author(s):  
Xian-Lun Tang ◽  
Wei-Chang Ma ◽  
De-Song Kong ◽  
Wei Li
Keyword(s):  
Supervised Learning ◽  
Motor Imagery ◽  
Adaptive Learning ◽  
Recognition Accuracy ◽  
Learning Rate ◽  
Data Sets ◽  
Adaptive Learning Rate ◽  
Eeg Data ◽  
Contrastive Divergence ◽  
Electroencephalogram Eeg

Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence. Prior knowledge is introduced into the intermediary layer of the deep stacking network, and a restricted Boltzmann machine is trained by a semisupervised method in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Several EEG data sets are carried out to evaluate the performance of the proposed method. The results show that the recognition accuracy of SADSN is advanced with a more significant convergence rate and successfully classifies motor imagery.

scholarly journals Motor Imagery EEG Classification Using Capsule Networks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2854 ◽  
Author(s):  
Kwon-Woo Ha ◽  
Jin-Woo Jeong
Keyword(s):  
Motor Imagery ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Left Hand ◽  
Computer Interfaces ◽  
Conventional Machine ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Target Data

Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

EEG Analysis of Imagined Speech

2020 ◽  
pp. 679-692
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

EEG Analysis of Imagined Speech

2016 ◽  
Vol 3 (2) ◽  
pp. 32-44
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

scholarly journals Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

Entropy ◽  
2018 ◽  
Vol 20 (1) ◽  
pp. 7 ◽  
Author(s):  
Rubén Martín-Clemente ◽  
Javier Olias ◽  
Deepa Thiyam ◽  
Andrzej Cichocki ◽  
Sergio Cruces
Keyword(s):  
Experimental Comparison ◽  
Information Theoretic ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Leibler Divergence ◽  
The Common ◽  
Alpha Beta ◽  
Class Labels ◽  
The Relationship ◽  
Electroencephalogram Eeg

Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.

Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface

2019 ◽  
Vol 29 (01) ◽  
pp. 1850014 ◽  
Author(s):  
Marie-Constance Corsi ◽  
Mario Chavez ◽  
Denis Schwartz ◽  
Laurent Hugueville ◽  
Ankit N. Khambhati ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Performance Enhancement ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Computer Interface ◽  
Computer Interfaces ◽  
The Impact ◽  
Electroencephalogram Eeg ◽  
Fusion Approach ◽  
Single Modality

We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain–computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

An empirical survey of electroencephalography-based brain-computer interfaces

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Megha M. Wankhade ◽  
Suvarna S. Chorage
Keyword(s):  
Motor Imagery ◽  
Visual Evoked Potential ◽  
Event Related Potential ◽  
Motor Impairments ◽  
Research Papers ◽  
Survey Paper ◽  
Empirical Survey ◽  
Computer Interfaces ◽  
Implementation Tool ◽  
Electroencephalogram Eeg

AbstractObjectivesThe Electroencephalogram (EEG) signal is modified using the Motor Imagery (MI) and it is utilized for patients with high motor impairments. Hence, the direct relationship between the computer and brain is termed as an EEG-based brain-computer interface (BCI). The objective of this survey is to presents an analysis of the existing distinct BCIs based on EEG.MethodsThis survey provides a detailed review of more than 60 research papers presenting the BCI-based EEG, like motor imagery-based techniques, spatial filtering-based techniques, Steady-State Visual Evoked Potential (SSVEP)-based techniques, machine learning-based techniques, Event-Related Potential (ERP)-based techniques, and online EEG-based techniques. Subsequently, the research gaps and issues of several EEG-based BCI systems are adopted to help the researchers for better future scope.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsThis survey paper exposes research topics on BCI-based EEG, which helps the researchers and scholars, who are interested in this domain.

scholarly journals Effect of a Brain–Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2020
Author(s):  
Vivianne Flávia Cardoso ◽  
Denis Delisle-Rodriguez ◽  
Maria Alejandra Romero-Laiseca ◽  
Flávia A. Loterio ◽  
Dharmendra Gurve ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Primary Motor Cortex ◽  
Cortical Excitability ◽  
Brain Connectivity ◽  
Sources Of Information ◽  
Beta Band ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Sensory Motor ◽  
On Line

Recently, studies on cycling-based brain–computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.

scholarly journals Towards ideal time window for classifying motor imagery in brain-computer interfaces

2020 ◽  
Author(s):  
Vitor Mendes Vilas-Boas ◽  
Vitor Da Silva Jorge ◽  
Cleison Daniel Silva
Keyword(s):  
Motor Imagery ◽  
Time Window ◽  
Brain Activity ◽  
Classification Performance ◽  
Brain Computer Interfaces ◽  
Temporal Properties ◽  
Computer Interfaces ◽  
Motor Intention ◽  
Eeg Data ◽  
The Individual

Brain-Computer Interfaces (ICM) allow the control of devices by modulating brain activity. Commonly, when based on motor imagery (IM) these systems use the energy (de)synchronization in the electroencephalogram signal (EEG), voluntarily caused by the individual, to identify and classify their motor intention. Therefore, the EEG segment used in the training of the learning algorithms plays a fundamental role in the description of the characteristics and, consequently, in the recognition of patterns in the signal. In this context, the objective of this work is to demonstrate the correlation between the temporal properties of the input EEG segment and the classification performance of a ICM-IM system. An auxiliary sliding window was used in order to obtain the variation of performance in function of the variation in the time and to support the decision making about the appropriate window. Simulations based on public EEG data point to significant variability in the location and width of the ideal window and suggest the need for individualized selection according to the cognitive patterns of each subject.

scholarly journals Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface

2020 ◽  
Author(s):  
Christoph Reichert ◽  
Stefan Dürschmid ◽  
Mandy V. Bartsch ◽  
Jens-Max Hopf ◽  
Hans-Jochen Heinze ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Right Visual Field ◽  
Spatial Filters ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Related Potentials ◽  
Left And Right ◽  
Viable Approach ◽  
Number Of Classes ◽  
Electroencephalogram Eeg

AbstractObjectiveOne of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze-direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts.ApproachWe present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component – a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure.Main resultsDespite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks.SignificanceOur method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.

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