Exploring the Potential of Electroencephalography Signal–Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis

Research on brain–computer interfaces (BCIs) advances the way scientists understand how the human brain functions. The BCI system, which is based on the use of electroencephalography (EEG) signals to detect motor imagery (MI) tasks, enables opportunities for various applications in stroke rehabilitation, neuroprosthetic devices, and communication tools. BCIs can also be used in emotion recognition (ER) research to depict the sophistication of human emotions by improving mental health monitoring, human–computer interactions, and neuromarketing. To address the low accuracy of MI-BCI, which is a key issue faced by researchers, this study employs a new approach that has been proven to have the potential to enhance motor imagery classification accuracy. The basic idea behind the approach is to apply feature extraction methods from the field of emotion recognition to the field of motor imagery. Six feature sets and four classifiers were explored using four MI classes (left and right hands, both feet, and tongue) from the BCI Competition IV 2a dataset. Statistical, wavelet analysis, Hjorth parameters, higher-order spectra, fractal dimensions (Katz, Higuchi, and Petrosian), and a five-dimensional combination of all five feature sets were implemented. GSVM, CART, LinearSVM, and SVM with polynomial kernel classifiers were considered. Our findings show that 3D fractal dimensions predominantly outperform all other feature sets, specifically during LinearSVM classification, accomplishing nearly 79.1% mean accuracy, superior to the state-of-the-art results obtained from the referenced MI paper, where CSP reached 73.7% and Riemannian methods reached 75.5%. It even performs as well as the latest TWSB method, which also reached approximately 79.1%. These outcomes emphasize that the new hybrid approach in the motor imagery/emotion recognition field improves classification accuracy when applied to motor imagery EEG signals, thus enhancing MI-BCI performance.

MSAttNet: Multi-scale attention convolutional neural network for motor imagery classification.

EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick

Existing research methods for recognizing EEG (Electroencephalogram) signals in motor imagery (MI) often overlook the dynamic changes of brain networks over time, resulting in insufficient classification accuracy for MI tasks. This article addresses the recognition problem of dynamic changes in brain networks during MI tasks and applies an EEG signal classification method based on multi-layer time-varying functional brain networks. This article uses the BCI (Brain-Computer Interface) Competition IV 2a dataset to preprocess the raw EEG signals through bandpass filtering and CSP (Common Spatial Pattern) algorithm. The EEG signals of the MI task are divided into 7 1-second time windows with a step size of 0.5 seconds. Within each time window, Pearson correlation coefficients between EEG channels can be calculated to generate corresponding brain networks, and multiple time-varying functional brain networks can be constructed by stacking the brain networks from multiple time windows. The network topology features, node degree, clustering coefficient, network efficiency, and multi-layer network features of each window can be extracted, including Multiplex Clustering Coefficient (MCC), Multiplex Participation Coefficient (MPC), and inter layer correlation coefficient. By dividing the dataset through 10 fold cross validation, the random forest algorithm can be used to classify and recognize four types of motion imagination tasks. The experimental results show that the average recognition rate of the article’s method in four types of MI tasks reached 89.19%. This method can improve the classification accuracy of MI tasks and enhance a comprehensive understanding of the dynamic changes in brain networks during the process of MI.

The electroencephalogram (EEG) signals often contaminated by muscle or electromyogram (EMG) artifacts. The presence of these artifacts obscure the desired information in the EEG signal. In this paper, we proposed an efficient subspace based technique named singular spectrum analysis (SSA), to remove the EMG artifacts from the single channel EEG signals. For this we proposed a new grouping technique to extract efficiently the desired component from the contaminated EEG signal by setting a threshold. Firstly, single channel signal is mapped into multichannel signal or data, called embedding. Next, the orthogonal eigenvectors are estimated from the covariance matrix of the multichannel data by singular value decomposition (SVD). Since the local variations of eigenvectors corresponding to the EEG signals are low compare with the EMG signals, we set an arbitrary threshold (0.275) to find these eigenvectors, which are used to create the subspace corresponding to the EEG signals. After identifying the subspace, the EEG signals are extracted by simply projecting the multichannel data onto this subspace followed by reverse process of embedding step. Finally, the proposed method is applied on synthetic noisy sinusoidal signals and EEG signals contaminated by the EMG artifacts. The results shows that the proposed method can efficiently removes the EMG artifacts without altering the desired components.

This study aims to analyze the relationship between brainwave activity and the learning readiness of students with disabilities by utilizing EEG (Electroencephalography) signals. EEG signals are used to detect the brain's electrical activity that reflects mental states, including focus, concentration, and readiness to receive learning. This study was conducted at SLB 'Aisyiyah Krian with a quantitative approach through EEG signal measurement before and during the learning process. The results showed a significant correlation between the dominance of alpha and beta waves with learning readiness, while the dominance of theta and delta waves indicated unpreparedness. These findings provide a foundation for the development of a more inclusive, objective, and adaptive neurotechnology-based learning approach for students with disabilities.

IntroductionIn the field of brain-computer interfaces (BCI), motor imagery (MI) classification is a critically important task, with the primary objective of decoding an individual's MI intentions from electroencephalogram (EEG) signals. However, MI decoding faces significant challenges, primarily due to the inherent complex temporal dependencies of EEG signals.MethodsThis paper proposes a temporal fusion attention network (TFANet), which aims to improve the decoding performance of MI tasks by accurately modeling the temporal dependencies in EEG signals. TFANet introduces a multi-scale temporal self-attention (MSTSA) mechanism that captures temporal variation in EEG signals across different time scales, enabling the model to capture both local and global features. Moreover, the model adaptively adjusts the channel weights through a channel attention module, allowing it to focus on key signals related to motor imagery. This further enhances the utilization of temporal features. Moreover, by integrating the temporal depthwise separable convolution fusion network (TDSCFN) module, TFANet reduces computational burden while enhancing the ability to capture temporal patterns.ResultsThe proposed method achieves a within-subject classification accuracy of 84.92% and 88.41% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively. Furthermore, using a transfer learning approach on the BCIC-IV-2a dataset, a cross-subject classification accuracy of 77.2% is attained.ConclusionThese results demonstrate that TFANet is an effective approach for decoding MI tasks with complex temporal dependencies.

Human emotion recognition could greatly contribute to human–computer interaction with promising applications in artificial intelligence. One of the challenges in recognition tasks is learning effective representations with stable performances from electroencephalogram (EEG) signals. In this article, we propose a novel deep-learning framework, named channel-fused dense convolutional network, for EEG-based emotion recognition. First, we use a 1-D convolution layer to receive weighted combinations of contextual features along the temporal dimension from EEG signals. Next, inspired by state-of-the-art object classification techniques, we employ 1-D dense structures to capture electrode correlations along the spatial dimension. The developed algorithm is capable of handling temporal dependencies and electrode correlations with the effective feature extraction from noisy EEG signals. Finally, we perform extensive experiments based on two popular EEG emotion datasets. Results indicate that our framework achieves prominent average accuracies of 90.63% and 92.58% on the SEED and DEAP datasets, respectively, which both receive better performances than most of the compared studies. The novel model provides an interpretable solution with excellent generalization capacity for broader EEG-based classification tasks.

One of the essential challenges in brain–computer interface is to classify motor imagery (MI) signals. In this paper, an ensemble SVM-based voting system is proposed. In each line of this system, the EEG signal is transformed into different representations based on discrete cosine transform, Fourier transform, common spatial pattern, and empirical mode decomposition, and then these representations are combined in a triple-frame matrix. These frames are fed into a pre-trained deep convolutional neural network as a feature extractor. For each line, an SVM is employed to classify the extracted feature vectors. Finally, a decision is made based on voting between these SVMs. Performance of the proposed method is examined on the BCI Competition III dataset Iva to separate right hand and foot movement imagery. The simple proposed method achieves the average accuracy of 96.34% for all of the subjects, and 99.70% for the best situation that is an improvement in MI classification. In addition, it can be seen that right side of the brain is more effective than the other side in EEG-based MI classification.

By 2021, WHO projects over a billion incapacitated people, with 20% facing daily functional impairments.Brain-Computer Interface (BCI) offers effortless machine control via direct brain-computer interaction, with Motor Imagery (MI) Electroencephalogram (EEG) as the key BCI foundation.The MI EEG signals were collected from the BNCI horizon2020 database for nine participants.The MI EEG data includes four tasks: imaginative movement of the left hand, right hand, feet, and tongue.There are 22 EEG channels with a sampling rate of 250Hz in the data.The MI EEG signals were band-pass filtered with a lower cut-off frequency of 0.5 Hz and an upper cut-off frequency of 100 Hz.Based on the energy count threshold approach (ECTA) nine channels were identified as dominating channels from the filtered MI EEG signals.Energy values for each channel were extracted in the ECTA method for the 3-sec window.And if the energy value of a channel for a particular window is greater than 60% of the maximum channel's energy, then the energy count value will be incremented by one.Finally whichever channels had larger energy counts were identified as a dominant channel.On these nine dominant MI EEG signals discrete wavelet transform using Daubechies 4 mother wavelet a four-level decomposition is applied, and Mu and beta rhythms were extracted.For a 3-sec window from the nine dominant channels, the energy and entropy feature values from the Mu and Beta rhythms were extracted.From the extracted features, 80% of the data is used for training Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) and 20% for testing classifier models.Both the models performed well on the test data and the results obtained had a highest accuracy 91.72.7 for subject-9 and these results were compared with the existing methods.The obtained results of MI EEG signals classification using BCI holds potential for revolutionizing assistive technology, stroke rehabilitation, virtual reality gaming,

Brain-computer interfaces can be used to operate devices by detecting a person's intention from their brain activity. Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a commonly used approach for this purpose. To reliably identify MI from EEG signals, a sufficient number of sensors is usually required. However, a large number of sensors increases the computational cost of discriminating MI classes. Furthermore, consumer-grade devices that measure EEG signals often employ a reduced number of sensors compared to medical- or research-grade devices. In this experimental study, we investigate the tradeoff between accuracy and complexity when decoding MI from a restricted number of EEG sensors. For this purpose, several decoding pipelines were trained on EEG data using different subsets of electrode locations employing well-established decoding methods. We found that there is no significant difference <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{(\mathrm{p}=[0.18-0.91])}$</tex> in average decoding accuracy when using fewer sensors. The largest loss in performance for a single individual was a reduction in mean decoding accuracy of 0.1 when using 8 out of 64 available sensors. Decoding MI from a limited number of sensors is therefore feasible, highlighting the potential of using commercial sensor devices for this purpose to reduce both monetary and computational costs.

The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different features, EEG signals are extracted using first-order extraction and are classified using the Neural Network method. The results of this study are typical of the five first-order features used, namely average, standard deviation, skewness, kurtosis, and entropy. The results of pattern recognition training show that 171 successful iterations are carried out with a period of execution of 6 seconds. Performance tests are performed using the Mean Squared Error (MSE) function. The results of the performance tests that were successfully obtained in the pattern test are in the number 0.000994.

ObjectiveElectroencephalogram (EEG) based brain–computer interfaces (BCI) in motor imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is essential because of a low signal-to-noise ratio (SNR) and time-dependent covariates of EEG signals. Because of efficient application in various fields, deep learning has been adopted in EEG signal processing and has obtained competitive results compared with the traditional methods. However, designing and training an end-to-end network to fully extract potential features from EEG signals remains a challenge in MI.ApproachIn this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification. We introduce a layered end-to-end network structure, in which a feature-extraction network is used to extract temporal and spatial features. To enhance the transfer learning ability, we propose a network initialization and fine-tuning strategy to train an individual model for inter-subject classification on small datasets. We compare our MSFBCNN with the state-of-the-art approaches on open datasets.ResultsThe proposed method has a higher accuracy than the baselines in intra-subject classification. In addition, the transfer learning experiments indicate that our network can build an individual model and obtain acceptable results in inter-subject classification. The results suggest that the proposed network has superior performance, robustness, and transfer learning ability.

Aiming to develop an noninvasive BMI control system with EEG (electroencephalogram) signals to control external devices such as prostheses and robots for rehabilitation and/or power support, four different tasks corresponding to different brain excitation degrees are designed. Their EEG spectra are analyzed with short-time fast Fourier transform (STFFT), and their features of mu and beta rhythms corresponding to the different tasks are extracted. The extracted features are used to control a one-joint robot arm and their corresponding results are compared. The results show that the EEG signal when a subject is holding a weight is comparatively more stable than the EEG signals in other tasks such as motor imagery. This implies an effective way for power assist by EEG signals.

Electroencephalography (EEG) is a non-invasive technique that records brain electrical activity, providing critical insights into neural processes. In recent years, EEG has become integral to brain-computer interface (BCI) research. BCIs enhance human-computer interaction, support assistive solutions for people with disabilities, and enable novel clinical applications. Research in EEG-based BCIs involves several key components: signal acquisition, preprocessing, feature extraction, and classification. Advanced machine learning models, especially those that emphasize personalized and incremental learning approaches, are used to effectively decode EEG signals. This personalization accounts for individual variability and significantly improves model accuracy and robustness. Applications of EEG-based BCIs include emotion recognition, motor imagery for robot control, and EEG-to-text decoding. These applications use EEG signals to make significant advances in their respective fields. Emotion recognition improves human-computer interaction and mental health monitoring; motor imagery enables intuitive robotic control that assists individuals with motor impairments; and EEG-to-text decoding provides new communication pathways for people with severe disabilities. Despite promising advances, challenges such as signal variability, noise, and the need for sophisticated preprocessing techniques remain. Future research should prioritize interdisciplinary collaboration and technological advancements to overcome these challenges, thereby enabling EEG-based BCIs to achieve broader applicability and significantly impact various aspects of human life.