Showing papers on "Motor imagery published in 2022"
TL;DR: Transfer learning (TL) as discussed by the authors utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort.
Abstract: A brain–computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject nonstationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This article reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications—motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks—are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the article, which may point to future research directions.
54 citations
TL;DR: In this paper , a multi-scale convolutional neural network (MS-CNN) was proposed to extract distinguishable features of several non-overlapping canonical frequency bands of EEG signals from multiple scales for MI-BCI classification.
41 citations
40 citations
TL;DR: In this article , three hybrid models consisting of the convolutional neural networks (CNN) and the Long Short Term Memory (LSTM) were proposed to classify the EEG signal in the MI-based BCI.
36 citations
22 Dec 2022
TL;DR: Huang et al. as discussed by the authors proposed a novel deep common spatial pattern (DCSP) model with optimal objective function, which can transform data into another mapping with data of different categories having maximal differences in their measures of dispersion, and showed the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace.
Abstract: Survey/review study
Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function
Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,*
1 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom
2 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States
* Correspondence: Mengjie.Huang@liverpool.ac.uk
Received: 12 October 2022
Accepted: 28 November 2022
Published: 22 December 2022
Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.
29 citations
TL;DR: In this paper , a neural network feature fusion algorithm is proposed by combining the convolutional neural network (CNN) and the long-short-term memory network (LSTM), where the CNN and LSTM are connected in parallel.
26 citations
TL;DR: In this paper, a neural network feature fusion algorithm is proposed by combining the convolutional neural network (CNN) and the long-short-term memory network (LSTM), where the CNN and LSTM are connected in parallel.
26 citations
TL;DR: Wang et al. as discussed by the authors proposed a subject-independent generalized MLP model with ≈90% accuracy and half the classification time compared to traditional ML-based models, which suggests the possibility of a much accurate and robust generalized BCI (subject independent) if this model integrates sophisticated optimization.
24 citations
TL;DR: Wang et al. as discussed by the authors proposed a subject-independent generalized MLP model with ≈90% accuracy and half the classification time compared to traditional ML-based models, which suggests the possibility of a much accurate and robust generalized BCI (subject independent) if this model integrates sophisticated optimization.
24 citations
TL;DR: Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology, which shows that classical machine learning approaches are still effective for binary classifications.
Abstract: Objective. Processing strategies are analyzed with respect to the classification of electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor imagery (MI). A review of literature is carried out to understand the achievements in MI classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility. Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery-based BCIs. Article search was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses standard and 89 studies were included. Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85%–100% range for the binary case and in the 83%–93% range for multi-class one. Associated uncertainties are up to 6% while repeatability for a predetermined dataset is up to 8%. Reproducibility assessment was instead prevented by lack of standardization in experiments. Significance. By relying on the analyzed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a BCI. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of the results reproducibility.
24 citations
TL;DR: In this paper , a comparative study of EEG-based multiclass motor imagery classifiers based on Kullback-Leiber regularised Riemann Mean and support vector machine, hybrid one versus one classifier, linear discriminant analysis, and convolutional neural network is presented.
Abstract: This paper presents a comparative study of EEG-based multiclass motor imagery classifiers based on Kullback-Leiber regularised Riemann Mean and support vector machine, hybrid one versus one classifier, linear discriminant analysis, and convolutional neural network. The paper is felt to be of inter- est to those researchers working in the motor imagery classification of EEG signals. The work presented in this paper helps to understand the basics of different multi-class motor imagery classifiers, their accuracy, and the number of channels involved.
TL;DR: In this paper , a pretrained convolutional neural network (CNN)-based new automated framework was proposed for robust BCI systems with small and ample samples of motor and mental imagery EEG training data.
TL;DR: In this article , a novel end-to-end multi-task learning approach is proposed to enhance the EEG classification performance in a subject-independent manner, achieving an F1-score improvement of 6.72%, and 2.23% on the SMR-BCI and OpenBMI datasets.
Abstract: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite great advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72%, and 2.23% on the SMR-BCI, and OpenBMI datasets, respectively. We demonstrate that MIN2Net improves discriminative information in the latent representation. This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without the need for calibration.
TL;DR: This article proposes a novel deep learning-based lightweight model based on attention-inception convolutional neural network and long- short-term memory that achieves excellent accuracy on public competition datasets while requiring few parameters and low computational time.
Abstract: In recent years, the contributions of deep learning have had a phenomenal impact on electroencephalography-based brain-computer interfaces. While the decoding accuracy of electroencephalography signals has continued to increase, the process has caused deep learning models to continuously expand in terms of size and computational resource requirements. However, due to their increased size and computational requirements, it has become difficult to embed, store, and execute deep learning models for artificial intelligence of things, cloud-based, or edge devices used in rehabilitation. Hence, this article proposes a novel deep learning-based lightweight model based on attention-inception convolutional neural network and long- short-term memory. The proposed model achieves excellent accuracy on public competition datasets while requiring few parameters and low computational time. Using the BCI competition IV 2a dataset and the high gamma dataset, the proposed model achieved 82.8% and 97.1% accuracies, respectively.
TL;DR: This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE), a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses.
Abstract: Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data’s high-level features remains a difficulty. This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses. When compared to existing state-of-the-art EEG motor imagery classification models, the suggested model achieves good accuracy (82.87%) with reduced parameters in the BCI-IV2a motor imagery dataset and (96.15%) in the high gamma dataset.
TL;DR: The proposed multibranch CNN models outperformed other state-of-the-art EEG motor imagery classification methods and improved the accuracy of a single-scale network by 6.84%.
TL;DR: In this article , a manifold embedded transfer learning (METL) framework was proposed for motor imagery (MI) EEG decoding, where covariance matrices of the EEG trials are first aligned on the symmetric positive definite (SPD) manifold and Grassmann manifold.
TL;DR: The findings show promise for employment of DL models on raw EEG signals in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.
Abstract: Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). In recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. However, past BCI studies that employed DL models, only attempted them with a small group of participants, without investigating the effectiveness of this approach for different user groups such as inefficient users. BCI inefficiency is a known and unsolved problem within BCI literature, generally defined as the inability of the user to produce the desired SMR patterns for the BCI classifier. In this study, we evaluated the effectiveness of DL models in capturing MI features particularly in the inefficient users. EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp were recorded to compare the performance of two classification approaches; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. Additionally, subjects were divided into high vs. low performers based on their online BCI accuracy and the difference between the two classifiers’ performance was compared between groups. Our results showed that the CNN model improved the classification accuracy for all subjects within the range of 2.37 to 28.28%, but more importantly, this improvement was significantly larger for low performers. Our findings show promise for employment of DL models on raw EEG signals in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.
TL;DR: In this article , a 3D-CNN framework was proposed to preserve the multivariate structure and dependencies of the feature space of EEG data, and a layer-wise decomposition model was implemented using 3D CNN framework to secure reliable classification results on a single-trial basis.
Abstract: Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space of EEG data is highly dimensional and signal patterns are specific to the subject, appropriate methods for feature representation are required to enhance the decoding accuracy of the CNN model. Furthermore, neural changes exhibit high variability between sessions, subjects within a single session, and trials within a single subject, resulting in major issues during the modeling stage. In addition, there are many subject-dependent factors, such as frequency ranges, time intervals, and spatial locations at which the signal occurs, which prevent the derivation of a robust model that can achieve the parameterization of these factors for a wide range of subjects. However, previous studies did not attempt to preserve the multivariate structure and dependencies of the feature space. In this study, we propose a method to generate a spatiospectral feature representation that can preserve the multivariate information of EEG data. Specifically, 3-D feature maps were constructed by combining subject-optimized and subject-independent spectral filters and by stacking the filtered data into tensors. In addition, a layer-wise decomposition model was implemented using our 3-D-CNN framework to secure reliable classification results on a single-trial basis. The average accuracies of the proposed model were 87.15% (±7.31), 75.85% (±12.80), and 70.37% (±17.09) for the BCI competition data sets IV_2a, IV_2b, and OpenBMI data, respectively. These results are better than those obtained by state-of-the-art techniques, and the decomposition model obtained the relevance scores for neurophysiologically plausible electrode channels and frequency domains, confirming the validity of the proposed approach.
TL;DR: In this article , a novel method for the classification of motor imaging (MI) electroencephalography (EEG) signals are proposed for controlling a robotic arm using a brain-computer interface (BCI).
TL;DR: In this paper, a novel method for the classification of motor imaging (MI) electroencephalography (EEG) signals are proposed for controlling a robotic arm using a brain-computer interface (BCI).
TL;DR: In this article , the supplementary motor area (SMA) activation was measured using functional near infrared spectroscopy (fNIRS) and continuous wave (CW-) fMRI.
Abstract: Compared to functional magnetic resonance imaging (fMRI), functional near infrared spectroscopy (fNIRS) has several advantages that make it particularly interesting for neurofeedback (NFB). A pre-requisite for NFB applications is that with fNIRS, signals from the brain region of interest can be measured. This study focused on the supplementary motor area (SMA). Healthy older participants (N = 16) completed separate continuous-wave (CW-) fNIRS and (f)MRI sessions. Data were collected for executed and imagined hand movements (motor imagery, MI), and for MI of whole body movements. Individual anatomical data were used to (i) define the regions of interest for fMRI analysis, to (ii) extract the fMRI BOLD response from the cortical regions corresponding to the fNIRS channels, and (iii) to select fNIRS channels. Concentration changes in oxygenated ([Formula: see text]) and deoxygenated ([Formula: see text]) hemoglobin were considered in the analyses. Results revealed subtle differences between the different MI tasks, indicating that for whole body MI movements as well as for MI of hand movements [Formula: see text] is the more specific signal. Selection of the fNIRS channel set based on individual anatomy did not improve the results. Overall, the study indicates that in terms of spatial specificity and task sensitivity SMA activation can be reliably measured with CW-fNIRS.
TL;DR: Jia et al. as mentioned in this paper proposed a novel deep learning framework based on graph convolutional neural networks (GCNs) to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes.
Abstract: Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.
TL;DR: In this article , a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning was proposed to decode the forearm movement decoding from electroencephalography (EEG) signals.
Abstract: Highly sophisticated control based on a brain-computer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Ten healthy participants took part in an experiment and performed motor execution (ME) and motor imagery (MI) of the intuitive movement tasks (Dataset I). We propose a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning. We approached that training the reconstructed ME-EEG signals together will also achieve more sufficient classification performance with only a small amount of MI-EEG signals. The proposed CVNet was validated on our own Dataset I and a public dataset, BNCI Horizon 2020 (Dataset II). The classification accuracies of various movements are confirmed to be 0.83 (±0.04) and 0.69 (±0.04) for Dataset I and II, respectively. The results show that the proposed method exhibits performance increases of approximately 0.09~0.27 and 0.08~0.24 compared with the conventional models for Dataset I and II, respectively. The outcomes suggest that the training model for decoding imagined movements can be performed using data from ME and a small number of data samples from MI. Hence, it is presented the feasibility of BCI learning strategies that can sufficiently learn deep learning with a few amount of calibration dataset and time only, with stable performance.
TL;DR: This study proposes a noninvasive EEG-based BCI for a robotic arm control system that enables users to complete multitarget reach and grasp tasks and avoid obstacles by hybrid control and shows effective performance due to the combination of MI-based EEG, computer vision, gaze detection, and partially autonomous guidance.
Abstract: The controlling of robotic arms based on brain–computer interface (BCI) can revolutionize the quality of life and living conditions for individuals with physical disabilities. Invasive electroencephalography (EEG)-based BCI has been able to control multiple degrees of freedom (DOFs) robotic arms in three dimensions. However, it is still hard to control a multi-DOF robotic arm to reach and grasp the desired target accurately in complex three-dimensional (3D) space by a noninvasive system mainly due to the limitation of EEG decoding performance. In this study, we propose a noninvasive EEG-based BCI for a robotic arm control system that enables users to complete multitarget reach and grasp tasks and avoid obstacles by hybrid control. The results obtained from seven subjects demonstrated that motor imagery (MI) training could modulate brain rhythms, and six of them completed the online tasks using the hybrid-control-based robotic arm system. The proposed system shows effective performance due to the combination of MI-based EEG, computer vision, gaze detection, and partially autonomous guidance, which drastically improve the accuracy of online tasks and reduce the brain burden caused by long-term mental activities.
TL;DR: In this paper, a parallel convolutional neural network (PCNN) architecture is proposed to classify motor imagery signals, which achieves 83.0 ± 3.4% on BCI Competition IV dataset 2b, which outperforms the compared methods at least 5.2%.
TL;DR: In this article , a matrix determinant feature extraction approach for efficient classification of motor and mental imagery activities from electroencephalography (EEG) signals was introduced, where denoised data were sequentially arranged to form a square matrix of different orders (e.g., 10, 13, 16, and 20) and determinant was computed for each order matrix.
Abstract: This study introduces a novel matrix determinant feature extraction approach for efficient classification of motor and mental imagery activities from electroencephalography (EEG) signals. First, the multiscale principal component analysis was utilized to obtain clean EEG signals. Second, denoised data were sequentially arranged to form a square matrix of different orders (e.g.,10, 13, 16, and 20) and determinant was computed for each order matrix. Finally, the extracted matrix determinant features were provided to several machine learning and neural network classification models for classification. All experiments were carried out using a 10-fold cross-validation approach on three publicly accessible data sets: 1) data set IV-a; 2) data set IV-b; and 3) data set V of BCI competition III. Also, this study designs a computerized automatic detection of motor and mental imagery graphical user interface that can assist physicians/experts to efficiently analyses motor and mental imagery data. The experimental results reveal that the highest average classification accuracy of 99.55% (for data set IV-a), 99.52% (for data set IV-b), and 91.80% (for data set V) was obtained for motor and mental imagery, respectively, with 20-order matrix determinant using a feedforward neural network classifier. The experimental results suggest that the proposed framework provides a robust biomarker with the least computational complexity for the development of automated brain–computer interfaces.
TL;DR: In this paper , a parallel convolutional neural network (PCNN) architecture is proposed to classify motor imagery signals, which achieves 83.0 ± 3.4% on BCI Competition IV dataset 2b, which outperforms the compared methods at least 5.2%.
01 Apr 2022
TL;DR: In this paper , a dual-stream convolutional neural network (DCNN) was proposed to combine time-domain and frequency-domain features to improve the performance of motor imagery recognition.
Abstract: An important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot. In order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically. The experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs. Further analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.
TL;DR: In this article , an enhanced fusion framework was proposed to improve the MI-based brain computer interface (BCI) frameworks by adding an additional pre-processing step to the EEG signal, which makes it time-invariant.
Abstract: Brain Computer Interface technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery. In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. BCI systems are composed of a wide range of components that perform signal pre-processing, feature extraction and decision making. In this paper, we define a BCI Framework, named Enhanced Fusion Framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. Firstly, we include aan additional pre-processing step of the signal: a differentiation of the EEG signal that makes it time-invariant. Secondly, we add an additional frequency band as feature for the system and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing motor imagery-based brain-computer interface experiments. On this dataset, the new system achieved a 88.80% of accuracy. We also propose an optimized version of our system that is able to obtain up to 90,76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.