EEG Signal Processing Methods Research Articles

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.

Past, Present, and Future of EEG-Based BCI Applications.

An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

The role of electroencephalography electrical reference in the assessment of functional brain–heart interplay: From methodology to user guidelines

BackgroundThe choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet. MethodsThis study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient. ResultsThe SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement. Comparison with existing methodsThe use of EEG reference based on a common average outperforms on the use of other references for consistency in estimating directed functional BHI. We do not recommend the use of EEG references based on analytical derivations as the experimental conditions may not meet the requirements of their optimal estimation, particularly in clinical settings. ConclusionThe use of a common average for EEG electrical reference is concluded to be the most appropriate choice for a quantitative, functional BHI assessment.

EEG Assessment in Patients With Disorders of Consciousness: Aims, Advantages, Limits, and Pitfalls.

This study presents a brief review of literature exploring simple EEG-polygraphic examinations and procedures that can be carried out at a patient's bedside. These include EEG with a common electrode array and sleep evaluation. The review briefly discusses more complex analytical techniques, such as the application of advanced EEG signal processing methods developed by our research group, to define what type of consistent markers are suitable for clinical use or to better understand complex patient conditions. These advanced analytical techniques aim to detect relevant EEG-based markers that could be useful in evaluating patients and predicting outcomes. These data could contribute to future developments in research.

Computationally-Efficient Algorithm for Real-Time Absence Seizure Detection in Wearable Electroencephalography.

Advances in electroencephalography (EEG) equipment now allow monitoring of people with epilepsy in their daily-life environment. The large volumes of data that can be collected from long-term out-of-clinic monitoring require novel algorithms to process the recordings on board of the device to identify and log or transmit only relevant data epochs. Existing seizure-detection algorithms are generally designed for post-processing purposes, so that memory and computing power are rarely considered as constraints. We propose a novel multi-channel EEG signal processing method for automated absence seizure detection which is specifically designed to run on a microcontroller with minimal memory and processing power. It is based on a linear multi-channel filter that is precomputed offline in a data-driven fashion based on the spatial-temporal signature of the seizure and peak interference statistics. At run-time, the algorithm requires only standard linear filtering operations, which are cheap and efficient to compute, in particular on microcontrollers with a multiply-accumulate unit (MAC). For validation, a dataset of eight patients with juvenile absence epilepsy was collected. Patients were equipped with a 20-channel mobile EEG unit and discharged for a day-long recording. The algorithm achieves a median of 0.5 false detections per day at 95% sensitivity. We compare our algorithm with state-of-the-art absence seizure detection algorithms and conclude it performs on par with these at a much lower computational cost.

EEG Signal Analysis Methods and Their Applications

EEG is a weak physiological electrical signal, which has important value in clinical and laboratory research. This paper mainly introduces several common methods of EEG signal processing, including power spectrum analysis, time-frequency analysis, bispectral analysis, etc, it mainly introduces their principles and applications in EEG signal processing, and provides methods and approaches for studying EEG.

Novel joint algorithm based on EEG in complex scenarios

At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.

Reactive frequency band based movement imagery classification

This paper proposes a novel movement imagery feature extraction algorithm based on reactive frequency band (RFB) of user using electroencephalogram (EEG) signal for brain–computer interface (BCI). Based on the asymmetric nature of the human brain, the authors propose the algorithm for the feature extraction from EEG data related to the imagination of hand and leg movement. In this proposed algorithm, the RFB has been estimated by applying time–frequency series (TFS) based method over the training dataset. Further, based on this RFB, the TFS based feature extraction method has been applied to the testing dataset. An asymmetry coefficient based on Hjorth parameter as a feature extracted for each frequency bin obtained through the TFS, further subjected to support vector machine (SVM) classifier for their classification. The effectiveness of the proposed RFB estimation method of EEG signal is validated by self-generated artificial sine wave signal. The movement imagery classification method has been validated through the three open source BCI competition datasets, such as single subject, five subjects and nine subjects movement imagery dataset. The proposed method of EEG signal processing outperforms the different well established conventional methods of EEG signal processing like discriminative frequency band common spatial pattern (DFBCSP), least square support vector machine (LS-SVM), regularized common spatial pattern (R-CSP) etc. over the BCI competition II, III and IV dataset.

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis.

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke.

A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness.

Recently, neuroimaging technologies have been developed as important methods for assessing the brain condition of patients with disorders of consciousness (DOC). Among these technologies, resting-state electroencephalography (EEG) recording and analysis has been widely applied by clinicians due to its relatively low cost and convenience. EEG reflects the electrical activity of the underlying neurons, and it contains information regarding neuronal population oscillations, the information flow pathway, and neural activity networks. Some features derived from EEG signal processing methods have been proposed to describe the electrical features of the brain with DOC. The computation of these features is challenging for clinicians working to comprehend the corresponding physiological meanings and then to put them into clinical applications. This paper reviews studies that analyze spontaneous EEG of DOC, with the purpose of diagnosis, prognosis, and evaluation of brain interventions. It is expected that this review will promote our understanding of the EEG characteristics in DOC.

P300‐based deception detection in simulated network fraud condition

A deception detection experiment with three-stimulus guilty knowledge test paradigm in simulated network fraud condition was conducted. The raw electroencephalography (EEG) signals were acquired from 12 subjects during the deception detection experiment. Then a multi-domain EEG signal processing method was proposed, preprocessing the raw EEG signals and extracting features in temporal, spectral and spatial domains. Subsequently, genetic algorithm was implemented to obtain optimal feature subset and linear discriminant analysis was used for classification. Experiment results obtained by the proposed technique confirmed its effectiveness in deception detection under simulated network fraud condition.

Monopolar and Bipolar Electrode Settings for SSVEP-Based Brain-Computer Interface

This study ranks twelve electroencephalogram (EEG) electrodes, located in the parieto-occipital and occipital regions of the human scalp, according to their relevance in the detection of steady-state visually evoked potential (SSVEP). Monopolar and bipolar arrangements were used to evaluate the capability of these electrodes to improve the signal detection performance in SSVEP-based brain-computer interfaces (BCIs). EEG signals were acquired from 18 volunteers, 8 of whom had disabilities. The signals were analyzed using four EEG signal processing methods commonly used for SSVEP-based BCIs, namely independent component analysis, lock-in analyzer system, multiple channel detection, and spectral F test. The results show that Oz is the most suitable electrode in SSVEP detection. However, further investigation should be conducted for other EEG paradigms. The five most highly suitable bipolar settings are POz-Oz, POz-O1, POz-O2, PO3-Oz, and PO4-Oz.

Low-amplitude craniofacial EMG power spectral density and 3D muscle reconstruction from MRI

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a preliminary investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an anatomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding signal contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG signals. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditionally in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in signal contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT analysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG signals were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an anatomical model for the isolation and removal of confounding low-amplitude craniofacial EMG signals from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles.

Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI.

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a preliminary investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an anatomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding signal contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG signals. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditionally in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in signal contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT analysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG signals were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an anatomical model for the isolation and removal of confounding low-amplitude craniofacial EMG signals from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles.

Influence of stimuli colour on feature classification for BCI applications

One of the key points of Brain Computer Interface (BCI) system is how different Electroencephalogram (EEG) features are abstracted and distinguished. Therefore, EEG signal processing method is the focus of BCI. In this study, four different flickering frequencies in low-frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four stimuli colours, green, red, blue and violet, were used in this study to investigate the influence of colour on SSVEPs. The EEG signals recorded from the occipital region were segmented into 1 second windows and features were extracted by using wavelet transform. Support Vector Machines (SVMs) are used for classification. In colour comparison, SSVEP with the violet colour showed higher accuracy than that with other stimuli.

Application of the Hilbert_Huang Transform and Wavelet Transform in the EEG Processing

Brain-Machine Interface (BMI) could make people control machine through EEG which is produced by the brain activity, and it provide a new communication method between human and machine. The research for BMI will extend the ability of communication and control the environment and machine. The key point of the BMI is how to abstract and distinguish different EEG characters. Therefore, EEG signal processing method is the emphasis of BMI. Wavelet Transform and Hilbert-Huang Transform are used to analyze the EEG signal in this paper. The results indicate that these two methods could abstract the main characters of the EEG, but the Hilbert-Huang Transform could express the distributing status in the time and frequency aspect of the EEG more accurately, because it produces the self-adaptive basis according the data, and obtain the local and instantaneous frequency of the EEG.

Pilot Study: The Use of Electroencephalogram to Measure Attentiveness towards Short Training Videos

Universities, schools, and training centers are seeking to improve their computer-based [3] and distance learning classes through the addition of short training videos, often referred to as podcasts [4]. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. The proposed research presents a novel approach to this issue. Signal processing of electroencephalogram (EEG) has proven useful in measuring attentiveness in a variety of applications such as vehicle operation and listening to sonar [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. Additionally, studies have shown that EEG data can be correlated to the ability of participants to remember television commercials days after they have seen them [16]. Electrical engineering presents a possible solution with recent advances in the use of biometric signal analysis for the detection of affective (emotional) response [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]. Despite the wealth of literature on the use of EEG to determine attentiveness in a variety of applications, the use of EEG for the detection of attentiveness towards short training videos has not been studied, nor is there a great deal of consistency with regard to specific methods that would imply a single method for this new application. Indeed, there is great variety in EEG signal processing and machine learning methods described in the literature cited above and in other literature [28] [29] [30] [31] [32] [33] [34]. This paper presents a novel method which uses EEG as an input to an automated system that measures a participant’s attentiveness while watching a short training video. This paper provides the results of a pilot study, including a structured comparison of signal processing and machine learning methods to find optimal solutions which can be extended to other applications.

Identification of the Epileptogenic Zone from Stereo-EEG Signals: A Connectivity-Graph Theory Approach

In the context of focal drug-resistant epilepsies, the surgical resection of the epileptogenic zone (EZ), the cortical region responsible for the onset, early seizures organization, and propagation, may be the only therapeutic option for reducing or suppressing seizures. The rather high rate of failure in epilepsy surgery of extra-temporal epilepsies highlights that the precise identification of the EZ, mandatory objective to achieve seizure freedom, is still an unsolved problem that requires more sophisticated methods of investigation. Despite the wide range of non-invasive investigations, intracranial stereo-EEG (SEEG) recordings still represent, in many patients, the gold standard for the EZ identification. In this contest, the EZ localization is still based on visual analysis of SEEG, inevitably affected by the drawback of subjectivity and strongly time-consuming. Over the last years, considerable efforts have been made to develop advanced signal analysis techniques able to improve the identification of the EZ. Particular attention has been paid to those methods aimed at quantifying and characterizing the interactions and causal relationships between neuronal populations, since is nowadays well assumed that epileptic phenomena are associated with abnormal changes in brain synchronization mechanisms, and initial evidence has shown the suitability of this approach for the EZ localization. The aim of this review is to provide an overview of the different EEG signal processing methods applied to study connectivity between distinct brain cortical regions, namely in focal epilepsies. In addition, with the aim of localizing the EZ, the approach based on graph theory will be described, since the study of the topological properties of the networks has strongly improved the study of brain connectivity mechanisms.

VIGILANCE ANALYSIS BASED ON EEG SIGNALS: SEEKING FOR SUITABLE FEATURES

Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. EEG signals are recorded and divided into five-second long trials. From these EEG trials, we extract feature vectors containing a large set of features. Random forest is used to rank the plenty of features and select the most important ones for later classification. After dimension reduction, sample vectors are trained and classified by Support Vector Machine (SVM). The proposed framework explores different methods of EEG signal processing to discover the most suitable features for a real-time vigilance monitoring system. We investigate and compare three different kinds of features which are based on Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Fractal Dimension (FD), respectively. On datasets acquired from 5 subjects, our result shows the CWT-based features reveal the highest classification accuracy (may reach over 96%). The DWT and FD-based features are less time-consuming in computation, and also reveal good result of classification accuracy (over 90%).

The Effect of Acupuncture at PC-6 on the Electroencephalogram and Electrocardiogram

The present study aims to examine the effect of acupuncture stimulation of an acupuncture point (PC-6) and nonacupuncture point on electroencephalograms (EEGs) and electrocardiograms (ECGs). We used EEG in 10 healthy subjects to investigate cortical activation during stimulation of acupuncture points (neiguan: PC-6) and nonacupuncture points. Our most interesting finding was the marked differences of amplitude of EEG power between acupuncture points and nonacupuncture points stimulation. Wavelet transform was used as the EEG signal processing method, because it has advantages in a time domain and frequency domain characteristics analysis. EEGs were collected from 16 channels, and the alpha-wave (8-13 Hz), beta-wave (13-30 Hz), theta-wave (4-8 Hz) and delta-wave (0.5-4 Hz) were used as standards for frequency bands. According to the experiment results, EEG signals increased considerably after acupuncture stimulation; in each frequency band, the average amplitude was higher after acupuncture stimulation; ECG heart rates were faster by at least 10% after acupuncture stimulation. Consequently, it will be possible to verify the function of acupuncture stimulation on neiguan (acupuncture points; PC-6) more effectively.