Decoding and mapping of right hand motor imagery tasks using EEG source imaging

Automated classification of non-motor mental task in electroencephalogram based brain-computer interface using multivariate autoregressive model in the intrinsic mode function domain

Bioelectrical (EEG) and hemodynamic (fMRI-response) cerebral reactions to performance and imaginary motor tasks by right or left hand were analyzed in 15 right-handed healthy persons (21-39 years old). During actual movement the main fMRI-response was registered in the area of central gyrus of the hemisphere contralateral to the working hand. Areas of activation were also revealed in the supplemental motor area and the ipsilateral hemisphere of the cerebellum. EEG data showed coherence increase in high frequency alpha- and beta-bands in the activated hemisphere. In imaginary motor tasks the intensity and topography of fMRI-response became the more variable; response was decreased in the motor area and in cerebellum, they increased in the subcortical structures and in the parietal association zones. EEG changes were very variable in this situation also; it was observe an increase of EEG coherence in the right hemisphere for higher frequency of alpha and beta spectral bands. Changes of power spectrum parameters were similar to performance and imaginary motor tasks. Spectrum power and middle frequency of beta band were increased. Topographically these changes did not correspond to activated hemisphere and it was more in the left hemisphere. These changes were reflected nonspecific component of reaction.

Our findings indicate the potential use of the proposed data fusion framework utilizing the graph-based features in the hybrid BCI systems by making the motor imaginary inference more accurate and more robust.

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

Motor Imagery (MI)-based Brain Computer Interface (BCI) system is a potential technology for active neurorehabilitation of stroke patients by complementing the conventional passive rehabilitation methods. Research to date mainly focused on classifying left vs. right hand/foot MI of stroke patients. Though a very few studies have reported decoding imagined hand movement directions using electroencephalogram (EEG)-based BCI, the experiments were conducted on healthy subjects. Our work analyzes MI-based brain cortical activity from EEG signals and decodes the imagined hand movement directions in stroke patients. The decoded direction (left vs. right) of hand movement imagination is used to provide control commands to a motorized arm support on which patient's affected (paralyzed) arm is placed. This enables the patient to move his/her stroke-affected hand towards the intended (imagined) direction that aids neuroplasticity in the brain. The synchronization measure called Phase Locking Value (PLV), extracted from EEG, is the neuronal signature used to decode the directional movement of the MI task. Event-related desynchronization/synchronization (ERD/ERS) analysis on Mu and Beta frequency bands of EEG is done to select the time bin corresponding to the MI task. The dissimilarities between the two directions of MI tasks are identified by selecting the most significant channel pairs that provided maximum difference in PLV features. The training protocol has an initial calibration session followed by a feedback session with 50 trials of MI task in each session. The feedback session extracts PLV features corresponding to most significant channel pairs which are identified in the calibration session and is used to predict the direction of MI task in left/right direction. An average MI direction classification accuracy of 74.44% is obtained in performing the training protocol and 68.63% from the prediction protocol during feedback session on 16 stroke patients.

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.

Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of-the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with μ and β bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

Electroencephalography (EEG)-based brain computer interface (BCI) systems hold significant promise across diverse applications; however, their performance is compromised by pervasive physiological artifacts that degrade signal fidelity. While current deep neural networks (DNNs) improve artifact rejection, their high computational cost precludes deployment in wearable BCIs systems. Here, we introduce STAND-Net (Spiking Temporal Attention autoeNcoDer Network), a neuromorphic architecture that leverages event-driven spiking neurons to achieve ultra-efficient, high-fidelity EEG artifact removal. STAND-Net combines a spike-convolution encoder-decoder with leaky integrate-and-fire neurons to model spatiotemporal EEG dynamics, a dilation-enhanced residual backbone capturing long-range dependencies, and a spike-rate attention mechanism dynamically localizing artifacts via neuronal firing patterns. The system demonstrates >3.7 dB improvement in signal-to-distortion ratio over state-of-the-art methods across diverse artifacts while consuming 97.98% less power than comparable DNNs. Crucially, downstream BCI classification accuracy increased by 6.64% using STAND-Net-processed signals. This work establishes a neuromorphic framework for low-power and high quality EEG artifact removal in wearable BCI systems.

Objective. In recent years, electroencephalogram (EEG)-based brain–computer interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs. Approach. To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-nearest-neighbors (kNNs) and support vector machine (SVM). Main results. The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs. Significance. This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.

In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy.

Significance: Ensemble learning is a robust and powerful approach to solve a variety of classification problems. Its usage has increased dramatically in recent years but not seen extensive application in EEG based Brain computer interface (BCI) problems. There is a wide range of classifiers which may not perform well, when used separately for classification problem but outperforms state-of-art algorithm when used as an ensemble, i.e., Long-short Term Memory (LSTM) is considered as best learning algorithms when time is embedded in input but not shown outstanding performance when used individually for EEG classification. On the contrary, it provided real good results when used as an ensemble. Aim: Aim of this study is how EEG signals can be classified using Ensembles methods; its importance and usage are described with experimental results. Approach: The approach that is being used is, combining different classifiers, i.e. Support vector machines, Decision Trees, Random Forest, Long Short Term Memory (LSTM), Logistic Regression (LR) and see which classifiers work best with which ensemble technique for EEG classification’s problem. Datasets: Datasets are taken from well-known data resources, Kaggle, EEG data set of confused students. The second dataset is taken from GitHub having EEG signals with timestamps according to events, i.e., sound, light, etc. According to our results, the LSTM- ensemble outperformed all other algorithms in the case where time is embedded in data.

Many methods, such as a joystic and a head mounted display, are used to control unmanned aerial vehicles like drones, but in recent years, many studies are being conducted on electroencephalogram (EEG)-based brain-computer interface (BCI) control methods. The non-invasive BCI does not require surgery and has an excellent temporal resolution, which is advantageous for continuously understanding the intention of the user. However, when performing stimuli-based paradigms among several paradigms, there is a disadvantage in that users must constantly concentrate on watching external stimuli displayed on the monitor. Therefore, it is necessary to use an intuitive paradigm. Nevertheless, the intuitive paradigm has rarely been used to control the formation of swarm drones until now. The EEG-based brain-computer interface using intuitive paradigms includes motor imagery, visual imagery, and imagined speech. Thus, by comparing and analyzing four kinds of drone swarm formation control using visual imagery paradigm, we try to find a more efficient method for flight control of swarm drones. As a result, we found that the average of the 4-class classification performance was 76.4% and the highest accuracy was 83% between 8 and 13 Hz. From now on, this study will be a cornerstone for domestic and overseas related research.

The structure and functioning of the human brain has always attracted the attention of scientists. In the past 10-20 years, one of the popular areas of study related to the human brain is the Brain Computer Interface (BCI) systems. Different brain monitoring techniques such as EEG, fNIRS, MEG can be used to observe human brain activities in BCI systems. In addition to the differences in monitoring methods in BCI systems, the focus of system operation may also change. In some BCI systems, it may be preferable to focus on Motor Imagery in another system, while the focus is on the P300 signals in EEG data. This requires a differentiation between the systems during the operational phases of the BCI system. One of the most significant points of system differentiation is the classification stage of the BCI system. In this study, a compilation of classification methods commonly used in Brain Computer Interface systems based on EEG based Motor Imagery will be carried out. This study will provide researchers with information about the methods that can be used in their own systems for classification in Brain Computer Interface systems based on EEG based Motor Imagery.

Improved motor imagery brain-computer interface performance via adaptive modulation filtering and two-stage classification