Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP and VI are defined as a changing of brain signals when perceiving and memorizing visual information, respectively. These paradigms could be alternatives to the previous visual-based paradigms which have limitations such as fatigue and low information transfer rates (ITR). In this study, we analyzed VP and VI to investigate the possibility to control BCI. First, we conducted a time-frequency analysis with event-related spectral perturbation. In addition, two types of decoding accuracies were obtained with convolutional neural network to verify whether the brain signals can be distinguished from each class in the VP and whether they can be differentiated with VP and VI paradigms. As a result, the 6-class classification performance in VP was 32.56% and the binary classification performance which classifies two paradigms was 90.16%.
In this paper, the deep learning (DL) approach is applied to a joint training scheme for asynchronous motor imagery-based Brain-Computer Interface (BCI). The proposed DL approach is a cascade of one-dimensional convolutional neural networks and fully-connected neural networks (CNN-FC). The focus is mainly on three types of brain responses: non-imagery EEG (\textit{background EEG}), (\textit{pure imagery}) EEG, and EEG during the transitional period between background EEG and pure imagery (\textit{transitional imagery}). The study of transitional imagery signals should provide greater insight i…
A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data.
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (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 non-inv…
Zhejiang University（浙江大学）；Maebashi Institute of Technology（前桥工业大学）；Monash University（莫纳什大学）；Ningbo Research Institute, Zhejiang University（浙江大学宁波研究院）
Motor imagery classification is of great significance to humans with mobility impairments, and how to extract and utilize the effective features from motor imagery electroencephalogram(EEG) channels has always been the focus of attention. There are many different methods for the motor imagery classification, but the limited understanding on human brain requires more effective methods for extracting the features of EEG data. Graph neural networks(GNNs) have demonstrated its effectiveness in classifying graph structures; and the use of GNN provides new possibilities for brain structure connectio…
University of Engineering and Technology, Taxila（塔克西拉工程技术大学）；Children’s National Hospital（国家儿童医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫·扎耶德儿科外科创新研究所）；George Washington University（乔治华盛顿大学）
The loss of limb motion arising from damage to the spinal cord is a disability that could effect people while performing their day-to-day activities. The restoration of limb movement would enable people with spinal cord injury to interact with their environment more naturally and this is where a brain-computer interface (BCI) system could be beneficial. The detection of limb movement imagination (MI) could be significant for such a BCI, where the detected MI can guide the computer system. Using MI detection through electroencephalography (EEG), we can recognize the imagination of movement in a…
University of Essex（埃塞克斯大学）；University of Jaén（哈恩大学）
In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental …
Brain-computer interface (BCI) systems have potential as assistive technologies for individuals with severe motor impairments. Nevertheless, individuals must first participate in many training sessions to obtain adequate data for optimizing the classification algorithm and subsequently acquiring brain-based control. Such traditional training paradigms have been dubbed unengaging and unmotivating for users. In recent years, it has been shown that the synergy of virtual reality (VR) and a BCI can lead to increased user engagement. This study created a 3-class BCI with a rather elaborate EEG sign…
Pseudo Channel: Time Embedding for Motor Imagery Decoding
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University（天津大学精密测试技术及仪器国家重点实验室）；Research Group Neuroinformatic, Faculty of Computer Science, University of Vienna（维也纳大学计算机科学学院神经信息学研究组）；University of Vienna（维也纳大学）
Characterizing functional brain networks in humans during magnetophosphene perception. Dense electroencephalography (EEG, 128 channels) was performed in N=3 volunteers during high-level (50 mT) magnetic field (MF) exposure. Functional brain networks were reconstructed, at the cortical level from scalp recordings, using the EEG source connectivity method. Magnetophosphene perception appears to consistently activate the right inferior occipito-temporal pathway. This study provides the very first neuroimaging results characterizing magnetophosphene perception in humans. The use of dense-EEG sourc…
Indian Institute of Science（印度科学研究所）；PES University（PES大学）
This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues. The results obtained demonstrate the advantage of requiring the subjects to perform motor actions following the trials of imagery. By introducing motor actions in the protocol, the subjects are able to perform actual motor planning, rather than just visualizing the motor movement, thus greatly improving the ease with which the motor movements can be imagined. This study also probes the added advantage of administeri…
Motor imagery based brain-computer interfaces (MI-BCIs) allow the control of devices and communication by imagining different muscle movements. However, most studies have reported a problem of "BCI-illiteracy" that does not have enough performance to use MI-BCI. Therefore, understanding subjects with poor performance and finding the cause of performance variation is still an important challenge. In this study, we proposed predictors of MI performance using effective connectivity in resting-state EEG. As a result, the high and low MI performance groups had a significant difference as 23% MI per…
The perception of color is an important cognitive feature of the human brain. The variety of colors that impinge upon the human eye can trigger changes in brain activity which can be captured using electroencephalography (EEG). In this work, we have designed a multiclass classification model to detect the primary colors from the features of raw EEG signals. In contrast to previous research, our method employs spectral power features, statistical features as well as correlation features from the signal band power obtained from continuous Morlet wavelet transform instead of raw EEG, for the clas…
Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image. To test this hypothesis, we developed a closed-loop system to show images inferred from electrocorticograms using a visual semantic space. The successful control of the feedback images demo…
Recent studies indicate that the neurons involved in a cognitive task aren't locally limited but span out to multiple human brain regions. We obtain network components and their locations for the task of listening to music. The recorded EEG data is modeled as a graph, and it is assumed that the overall activity is a contribution of several independent subnetworks. To identify these intrinsic cognitive subnetworks corresponding to music perception, we propose to decompose the whole brain graph-network into multiple subnetworks. We perform this decomposition to a group of brain networks by perfo…
Subject-Level Heterogeneity in EEG Motor Imagery Decoding: A Large-Scale Benchmark and Portfolio-Based Reduction of the Search Space
Inclusive Brains；Wavestone（威斯顿）；Human Technology Foundation（人类技术基金会）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Biotech Dental Group（生物科技牙科集团）；IBM Technology（IBM技术公司）；IBM France Lab（IBM法国实验室）
Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery
University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；Basque Center for Cognition, Brain and Language (BCBL)（巴斯克认知、大脑与语言中心）；Ikerbasque, Basque Foundation for Science（伊克尔巴斯克基金会（巴斯克科学基金会））
Seeing the imagined: latent functional alignment in visual imagery decoding from fMRI data
Department of Biomedicine and Prevention, University of Rome Tor Vergata（罗马特拉维拉大学生物医学与预防系）；Tether Evo；A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞总医院生物医学成像中心）
In this study, 3D brain-computer interface (BCI) training platforms were used to stimulate the subjects for visual motion imagery and visual perception. We measured the activation brain region and alpha-band power activity when the subjects perceived and imagined the stimuli. Based on this, 4-class were classified in visual stimuli session and visual motion imagery session respectively. The results showed that the occipital region is involved in visual perception and visual motion imagery, and alpha-band power is increased in visual motion imagery session and decreased in visual motion stimuli…
Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders
IBM Technology（IBM技术）；IBM France Lab（IBM法国实验室）；Inclusive Brains（包容大脑）；Wavestone；Human Technology Foundation（人类科技基金会）；Computing and Mathematical Sciences Division（计算与数学科学部）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（Mohamed bin Zayed人工智能大学）；Institute for Artificial Intelligence, Biotech Dental Group（人工智能研究所，生物技术牙科集团）
Flicker Noise Spectroscopy (FNS) has been used for the analysis of electroencephalography (EEG) signal related to the movement imagination. The analysis of sensorimotor rhythms in time-frequency maps reveals the event-related desynchronization (ERD) and the post-movement event-related synchronization (ERS), observed mainly in the contralateral hemisphere to the hand moved for the motor imagery. The signal has been parameterized in accordance with FNS method. The significant changes of the FNS parameters, at the time when the subject imagines the movement, have been observed. The analysis of th…
Brain-computer interface (BCI) is used for communication between humans and devices by recognizing status and intention of humans. Communication between humans and a drone using electroencephalogram (EEG) signals is one of the most challenging issues in the BCI domain. In particular, the control of drone swarms (the direction and formation) has more advantages compared to the control of a drone. The visual imagery (VI) paradigm is that subjects visually imagine specific objects or scenes. Reduction of the variability among EEG signals of subjects is essential for practical BCI-based systems. I…
