STAND-Net: A Spiking Temporal Attention autoeNcoDer Network for Efficient EEG Artifact Removal.

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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.

A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulations, which are easy to cause visual fatigue. This paper presents an SSVEP dataset acquired at a wide frequency range from 1 to 60 Hz with an interval of 1 Hz using flickering stimuli under two different modulation depths. This dataset contains 64-channel EEG data from 30 healthy subjects when they fixated on a single flickering stimulus. The stimulus was rendered on an LCD display with a refresh rate of 240 Hz. Initially, the dataset was rigorously validated through comprehensive data analysis to investigate SSVEP responses and user experiences. Subsequently, BCI performance was evaluated through offline simulations of frequency-coded and phase-coded BCI paradigms. This dataset provides comprehensive and high-quality data for studying and developing SSVEP-based BCI systems.

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

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.

Current EEG based brain computer interface (BCI) systems have achieved successful control in up to 3 dimensions; however, the current sensor-based paradigm is not well suited for many rehabilitative and recreational applications that require motor imagination (MI) tasks of fine motor movements to be recognized. Therefore there is a great need to find complex MI tasks that are intuitive for BCI users to perform and that can be classified with high accuracy. In this paper we present our results on classifying four MI tasks of the right hand, flexion, extension, supination and pronation using a novel EEG source imaging approach. Using this approach we were able to improve the four-class classification of the four tasks by nearly 10% as compared to traditional sensor-based techniques.

Electroencephalography (EEG)-based brain–computer interface (BCI) systems are often affected by substantial inter-subject variability. These differences cause distribution shifts between the source domain and the target domain. As a result, the decoder’s generalization to unseen subjects is reduced. In online steady-state visual evoked potentials (SSVEP)-based BCI systems, the decoder must not only cope with inter-subject distribution shifts but also adapt rapidly. However, most existing methods require accumulating multiple trials before adaptation, which increases data acquisition and update latency and thus limits their practicality in online settings. To address these challenges, this study focuses on a practically important but insufficiently explored setting, which is unlabeled inter-subject SSVEP decoding with single-trial online adaptation, where immediate adaptation is required and multi-trial accumulation is impractical. For this setting, this study proposes a low-latency test-time adaptation algorithm that combines trial-wise online Euclidean alignment, entropy minimization, and pseudo-label frequency regularization. This integration supports single-trial adaptation under online constraints, without requiring target labels or trial buffering, thereby reducing adaptation latency while mitigating inter-subject distribution shift. Experiments on two public datasets using four backbone models show that the proposed method achieves an average accuracy of 75.70%, outperforming the non-adaptive baseline by 3.88%. These results indicate that the proposed method improves inter-subject SSVEP decoding accuracy and shows potential for online BCI applications.

Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC). We analyzed the performance of the new method on two public EEG datasets, namely BCI competition III dataset IVa which has five subjects and BCI competition IV dataset IIb which has nine subjects. Compared to the performance offered by the existing SRC method, the proposed method achieves average classification accuracy improvements of 21.568 and 14.38% for BCI competition III dataset IVa and BCI competition IV dataset IIb, respectively. Furthermore, our approach also shows better classification performance when compared to other competing methods for both datasets.

This study investigates the implementation of a spontaneous visual potential (SSVEP)-based brain-computer interface (BCI) system and its performance evaluation. EEG signals from 32 healthy subjects were collected using a standard <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10 \times 10$</tex> electrode configuration and different visual stimulation frequencies (6, 10, 12, and 15\Hz). This study compares two classification methods: a linear classifier (least squares method) and a one-dimensional convolutional neural network (CNN) to improve the accuracy of SSVEP-based stimulus detection. The EEG signals were preprocessed using a bandpass filter and a notch filter, followed by denoising using a common average reference (CAR) filter. Fast Fourier transform (FFT) was used for feature extraction. Experimental results show that after adjusting parameters such as kernel size, number of filters, and activation function, the average classification accuracy of CNN reached 87 %, while the linear classifier had a higher accuracy of 47 %. Studies using CNN to process electroencephalogram (EEG) data have shown that it suffers from convergence issues and poor generalization ability. The results validate the effectiveness of CNN in real-time brain-computer interface (BCI) applications, while also highlighting the importance of preprocessing and parameter tuning.

Processing of the movement related task under planning by artificial means provides a means to those people whose natural modality of performing the task is bottlenecked by physical disability or neuro-motor disorders. Electroencephalography (EEG) based Brain-Computer Interfacing (BCI) systems can be defined to be a non-muscular pathway to operate rehabilitative devices using motor imagery signals captured from the motor activation areas in the brain. Supervised learning can help in prediction of motor imagery actions by processing raw EEG signals. However, dimension of the feature space plays a crucial role in this process. Large dimensional features not only increase the computational complexity but also the presence of redundant features causes reduction in classification accuracy. In this work, we intend to select the relevant features from the feature vector obtained by Power Spectrum Density estimation of the left/right motor imagery signals. BCI Competition 2008 — Graz dataset B has been used as the source of raw EEG data. To achieve this goal, we have used single-objective as well as many-objective version of Differential Evolution which optimizes the classifier's performance in terms of five metrics obtained from the Confusion Matrix. Support Vector Machine is used for fitness evaluation of the chosen feature subset as well as for classification of mental states. This work demonstrates the superiority of many-objective Differential Evolution in improving the accuracy due to reduction in feature dimension from an average of 60.56% to 82.60% while processing time of a test EEG sample reduces from 6.1 milliseconds to 5.6 milliseconds. The results obtained in this work are validated using Friedman Test.

Numerous electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems are being used as alternative means of communication for locked-in patients. Beyond these, BCIs are also considered in the context of post-stroke motor rehabilitation. Such research usually focuses on exploiting information decoded from sensorimotor activity of the brain. Here, we propose to extend this current focus beyond sensorimotor to also include associative brain areas. In this pilot study, we present an adaptive neurofeedback training paradigm to up-regulate particular EEG activity that is likely to enhance post-stroke motor rehabilitation. Our experimental results support the interpretation that the neurofeedback paradigm enables subjects to up-regulate intended activity and sustain that modulation in inter-trial resting periods in a state that we believe can support motor learning performance. These results serve as a beginning on viability of our claim on integrating a neurofeedback approach to BCI-based motor rehabilitation protocols.

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.

One of the main problems in Brain Computer Interface (BCI) systems is the non-stationary behavior of the electroencephalography (EEG) signals causing problems in real time applications. Another common problem in BCI systems is the situation where the labeled data are scarce. In this study, we take a semi-supervised learning perspective and propose solving both types of problems by updating the BCI system with labels obtained from the outputs of the classifier. To test the approach, data from motor imagery BCI system are used. Attributes extracted from EEG signals are classified with Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). With respect to the static classifiers, accuracy was improved approximately 4% using the proposed adaptation approach in the case of a training dataset. Even though the difference between the performance of static and adaptive classifiers decreases as the size of training data increases, the accuracy of our proposed adaptive classifier remains higher. The proposed approach has also improved the performance of a BCI system around 4% in the case of non-stationary signals as well.

Deep Neural Networks (DNNs) have been successfully applied to various real-world machine learning applications. However, performing large DNN inference tasks in real-time remains a challenge due to its substantial computational costs. Recently, Spiking Neural Networks (SNNs) have emerged as an alternative way of processing DNN'fs task. Due to its eventbased, data-driven computation, SNN reduces both inference latency and complexity. With efficient conversion methods from traditional DNN, SNN exhibits similar accuracy, while leveraging many state-of-the-art network models and training methods. In this work, an efficient neuromorphic hardware architecture for image recognition task is presented. To preserve accuracy, the analog-to-spiking conversion algorithm is adopted. The system aims to minimize hardware area cost and power consumption, enabling neuromorphic hardware processing in edge devices. Simulation results have shown that, with the MNIST digit recognition task, the system has achieved $\times 20$ reduction in terms of core area cost compared to the state-of-the-art works, with an accuracy of 94.4%, core area of 15 $\mu m^{2}$ at a maximum frequency of 250 MHz.