Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach

Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis

This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens.

Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition

Tensor analysis can comprehensively retain multidomain characteristics, which has been employed in EEG studies. However, existing EEG tensor has large dimension, making it difficult to extract features. Traditional Tucker decomposition and Canonical Polyadic decomposition(CP) decomposition algorithms have problems of low computational efficiency and weak capability to extract features. To solve the above problems, Tensor-Train(TT) decomposition is adopted to analyze the EEG tensor. Meanwhile, sparse regularization term can then be added to TT decomposition, resulting in a sparse regular TT decomposition (SR-TT). The SR-TT algorithm is proposed in this paper, which has higher accuracy and stronger generalization ability than state-of-the-art decomposition methods. The SR-TT algorithm was verified with BCI competition III and BCI competition IV dataset and achieved 86.38% and 85.36% classification accuracies, respectively. Meanwhile, compared with traditional tensor decomposition (Tucker and CP) method, the computational efficiency of the proposed algorithm was improved by 16.49 and 31.08 times in BCI competition III and 20.72 and 29.45 times more efficient in BCI competition IV. Besides, the method can leverage tensor decomposition to extract spatial features, and the analysis is performed by pairs of brain topography visualizations to show the changes of active brain regions under the task condition. In conclusion, the proposed SR-TT algorithm in the paper provides a novel insight for tensor EEG analysis.

Design of virtual BCI channels based on informer

The precision and reliability of electroencephalogram (EEG) data are essential for the effective functioning of a brain-computer interface (BCI). As the number of BCI acquisition channels increases, more EEG information can be gathered. However, having too many channels will reduce the practicability of the BCI system, raise the likelihood of poor-quality channels, and lead to information misinterpretation. These issues pose challenges to the advancement of BCI systems. Determining the optimal configuration of BCI acquisition channels can minimize the number of channels utilized, but it is challenging to maintain the original operating system and accommodate individual variations in channel layout. To address these concerns, this study introduces the EEG-completion-informer (EC-informer), which is based on the Informer architecture known for its effectiveness in time-series problems. By providing input from four BCI acquisition channels, the EC-informer can generate several virtual acquisition channels to extract additional EEG information for analysis. This approach allows for the direct inheritance of the original model, significantly reducing researchers' workload. Moreover, EC-informers demonstrate strong performance in damaged channel repair and poor channel identification. Using the Informer as a foundation, the study proposes the EC-informer, tailored to BCI requirements and demanding only a small number of training samples. This approach eliminates the need for extensive computing units to train an efficient, lightweight model while preserving comprehensive information about target channels. The study also confirms that the proposed model can be transferred to other operators with minimal loss, exhibiting robust applicability. The EC-informer's features enable original BCI devices to adapt to a broader range of classification algorithms and relax the operational requirements of BCI devices, which could facilitate the promotion of the use of BCI devices in daily life.

Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases

Software such as EEGLab has enabled the treatment and visualization of the tracing and cortical topography of the electroencephalography (EEG) signals. In particular, the topography of the cortical electrical activity is represented by colors, which make it possible to identify functional differences between cortical areas and to associate them with various diseases. The use of cortical topography with EEG origin in the investigation of diseases is often not used due to the representation of colors making it difficult to classify the disease. Thus, the analyses have been carried out, mainly, based on the EEG tracings. Therefore, a computer system that recognizes disease patterns through cortical topography can be a solution to the diagnostic aid. In view of this, this study compared five models of Convolutional Neural Networks (CNNs), namely: Inception v3, SqueezeNet, LeNet, VGG-16 and VGG-19, in order to know the patterns in cortical topography images obtained with EEG, in Parkinson's disease, Depression and Bipolar Disorder. SqueezeNet performed better in the 3 diseases analyzed, with Parkinson's disease being better evaluated for Accuracy (88.89%), Precison (86.36%), Recall (91.94%) and F1 Score (89.06%), the other CNNs had less performance. In the analysis of the values of the Area under ROC Curve (AUC), SqueezeNet reached (93.90%) for Parkinson's disease, (75.70%) for Depression and (72.10%) for Bipolar Disorder. We understand that there is the possibility of classifying neurological diseases from cortical topographies with the use of CNNs and, thus, creating a computational basis for the implementation of software for screening and possible diagnostic assistance.

Recognizing Missing Electromyography Signal by Data Split Reorganization Strategy and Weight-Based Multiple Neural Network Voting Method

Surface electromyography (sEMG) signals have been applied widely in prosthetic hand controlling. In the sEMG signal acquisition, wireless devices bring convenience, but also introduce signal missing due to interference or failure during data transmission. The missing signal may only last for tens of milliseconds, but have a great impact on the recognition. Researchers have employed various methods to complete missing sEMG data, but the completed signal may not totally fit the origins, and more extra calculation time will be spent. When recognizing hand gestures by sEMG from few sensors, to recognize the slightly or not serious signal missing, this study proposed a data split reorganization (DSR) strategy and a weight-based multiple neural network voting (WMV) method. To validate the proposed methods, controllable missing sEMG signals are generated artificially. Three time domain features are extracted based on non-overlapping sliding windows. The DSR is employed to make full use of the features, and then the WMV is utilized to recognize them. Nine subjects participated in the experiments, and the results indicate that the accuracy of the proposed methods is higher. For 5%, 10%, and 15% data missing ratios, the accuracy is 93.66%, 92.55%, and 91.19%, respectively. The Wilcoxon signed-rank test also demonstrates that these results are significantly superior to the situations in which the proposed methods are not applied. In the future, we will optimize the proposed methods to recognize the seriously missing sEMG signal.

Robust learning from corrupted EEG with dynamic spatial filtering

Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often encountered in consumer-grade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4,000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor the effective channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.

Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

Acquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation

The discriminative spatial patterns (DSP) algorithm is a classical and effective feature extraction technique for decoding of voluntary finger premovements from electroencephalography (EEG). As a purely data-driven subspace learning algorithm, DSP essentially is a spatial-domain filter and does not make full use of the information in frequency domain. The paper presents multilinear discriminative spatial patterns (MDSP) to derive multiple interrelated lower dimensional discriminative subspaces of low frequency movement-related cortical potential (MRCP). Experimental results on two finger movement tasks' EEG datasets demonstrate the effectiveness of the proposed MDSP method.

Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.

Different Approaches to SCADA Data Completion in Water Networks

This work contributes to the techniques used for SCADA (Supervisory Control and Data Acquisition) system data completion in databases containing historical water sensor signals from a water supplier company. Our approach addresses the data restoration problem in two stages. In the first stage, we treat one-dimensional signals by estimating missing data through the combination of two linear predictor filters, one working forwards and one backwards. In the second stage, the data are tensorized to take advantage of the underlying structures at five minute, one day, and one week intervals. Subsequently, a low-range approximation of the tensor is constructed to correct the first stage of the data restoration. This technique requires an offset compensation to guarantee the continuity of the signal at the two ends of the burst. To check the effectiveness of the proposed method, we performed statistical tests by deleting bursts of known sizes in a complete tensor and contrasting different strategies in terms of their performance. For the type of data used, the results show that the proposed data completion approach outperforms other methods, the difference becoming more evident as the size of the bursts of missing data grows.