EEG Signal Acquisition Research Articles

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review.

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

BCI‐control and monitoring system for smart home automation using wavelet classifiers

Brain Computer Interface (BCI) is a major research field that is based upon Electroencephalography (EEG) brain signals, which are captured using EEG electrodes, amplified and filtered before being converted to the digital form in order to perform thorough pre-processing and machine-learning. In this study, the design and implementation of the BCI control and monitoring system for smart home automation using wavelet features, which is based upon a dual-channel analogue EEG signal acquisition module is reported. The designed analogue EEG module performs EEG signal acquisition, signal amplification and filtering. Although the EEG data set contains thousands of samples and more than 15 different classes, we limit our study on 226 samples grouped into seven classes with 8-second time duration per sample. With careful settings of deep-learning classifier model parameters, the training and testing were successful with high accuracy results. The designed BCI system has several advantages including a large bandwidth of 400 Hz, low number of EEG electrodes, easy setup, simple user interface, pre-processing and digital filtering, fast machine learning, multi-class identification, monitoring and control models, high classification accuracy and low cost. This research work provided several contributions including the creation of recent and original EEG data set using well-labelled recordings at an adequate sampling rate of 2 kHz. The EEG signal acquisition module with 400-Hz bandwidth provides precise and rich EEG signal information needed for feature extraction. Our results are reproducible and have been tested and deployed on Raspberry pi 4 with Python. The designed wavelet-based BCI system consists of analogue EEG signal acquisition and machine-learning modules, which consist of deep-learning Multi-layer perceptron (MLP) classifiers and linear discriminant analysis (LDA) as well as other classifier models for comparison including convolutional neural networks (CNN). The deep learning and LDA classifiers models produced the best performance with average accuracy of 95.6% and 96% for both training and testing data sets.

An improved feature extraction algorithms of EEG signals based on motor imagery brain-computer interface

The electroencephalogram (EEG) signals based on the Brian-computer Interface (BCI) equipment is weak, non-linear, non-stationary and time-varying, so an effective feature extraction method is the key to improving the recognition accuracy. Electrooculogram and electrocardiogram artifacts are common noises in the process of EEG signals acquisition, it seriously affects the extraction of useful information. This paper proposes a processing method on EEG signals by combing independent component analysis (ICA), wavelet transform (WT) and common spatial pattern (CSP). First, the independent component analysis algorithm is used to break the EEG signals into independent components; and then these independent components are decomposed by WT to obtain the wavelet coefficient of each independent source. The soft and hard compromise threshold function is used to process the wavelet packet coefficients. Then the CSP algorithm is used to extract the features of the denoised EEG data. Finally, four common classification algorithms are used for classification to verify the effectiveness of the improved algorithm. The experimental results show that the EEG signals processed by the proposed method has obvious advantages in identify and remove electrooculogram (EOG) and electrocardiogram (ECG) artifacts, meanwhile, it can preserve the neural activity that is missed in the noise component. Cross-comparison experiments also proved that the proposed method has higher classification accuracy than other algorithms.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals.

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

Open-source concealed EEG data collection for Brain-computer-interfaces - neural observation through OpenBCI amplifiers with around-the-ear cEEGrid electrodes

ABSTRACT Observing brain activity in real life offers exciting possibilities like the support of physical health, mental well-being, and thought-controlled interaction modalities. The development of such applications is, however, strongly impeded by low accessibility to research-grade neural data and a lack of easy-to-use and comfortable sensors. To overcome these challenges, we present the cost-effective adaptation of concealed around-the-ear EEG electrodes (cEEGrids) to the open-source OpenBCI EEG signal acquisition platform. The system combines publicly available electronic components with 3D-printed parts, forming an easily replicable recording system for prolonged use and flexible application development. To demonstrate the system’s feasibility, experimentally induced changes in visual stimulation and mental workload are replicated. Lastly, a novel application area is investigated, the observation of flow experiences through observation of temporal Alpha power changes. Support for a link between temporal Alpha power and flow is found, indicating an efficient engagement of verbal-analytic reasoning with intensified flow levels.

Electroencephalogram signal characterization of tinnitus patients based on sample entropy algorithm and wavelet transform

Tinnitus is a disembodied, abnormal sound hallucination in the ear or skull, such as buzzing or hissing, in the absence of an external sound source. Tinnitus is a subjective sensation with no objective observable signs, and its causes are extremely complex. This paper explores the differences in sample entropy of EEG signals between tinnitus patients and normal subjects at different electrodes, using the non-parametric test Kruskal-Wallis test to find areas where there are significant differences between the different lateral tinnitus groups and the control group. Thirty tinnitus patients and 10 healthy controls were used to participate in the scalp EEG signal acquisition. The wavelet transform was first chosen to obtain the activity of each frequency band of the EEG, and then the electrophysiological differences between the two experimental groups were investigated by comparing the sample entropy of the EEG of the tinnitus patients with that of the healthy controls. The results reflect significant differences (p<0.05) in tinnitus patients at FT7, T7, C5, C6, TP7 and CP5 electrodes, mainly in the Delta band. These results compare the abnormalities of sample entropy in the resting state of patients with tinnitus on different sides of the ear with those of controls from an electroneuro-physiological perspective, and are expected to be used as a potential characteristic indicator to distinguish normal people from tinnitus patients for the auxiliary diagnosis of tinnitus and to provide physicians with an aid in diagnosing depressed patients.

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal.

In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring.

A dry electroencephalogram electrode for applications in steady-state visual evoked potential-based brain–computer interface systems

High-efficiency electroencephalogram (EEG) dry electrodes are a key component of brain–computer interface (BCI) technology because of their direct contact with the scalp. In this study, a semi-flexible polydopamine (PDA)/Pt-TiO2 electrode is prepared for the dry-contact acquisition of EEG signals. The PDA biofilm adheres strongly to the scalp and maintains a dynamic balance of water and ions. The Pt nanoparticles and TiO2 nanotube array together result in fast electron transfer. Therefore, the interface impedance between the dry PDA/Pt-TiO2 electrode and scalp is as low as 19.63–24.53 kΩ. The spontaneous EEG signal collected simultaneously using the dry PDA/Pt-TiO2 and wet Ag/AgCl electrodes had a correlation coefficient of up to 99.9%. In a steady-state visual evoked potential (SSVEP)-based BCI system, the dry electrode was used to collect EEG feedback signals for stimulations at 27 different frequencies in the range of 7–19.25 Hz. For these feedback signals, O1, Oz, and O2 channels in the occipital area exhibited high signal-to-noise ratios of 11.3, 11.8, and 11 dB, respectively. A volunteer wore an EEG headband with three PDA/Pt-TiO2 dry electrodes and successfully controlled the robotic arm of the SSVEP-BCI system in the untrained mode. The dry PDA/Pt-TiO2 electrode-based EEG cap is comfortable to wear, the identification signals of the SSVEP paradigm are accurate, and it is suitable for controlling external devices including a keyboard in the SSVEP-BCI system.

A Portable Waterproof EEG Acquisition Device for Dolphins.

The acquisition and analysis of EEG signals of dolphins, a highly intelligent creature, has always been a focus of the research of bioelectric signals. Prevailing cable-connected devices cannot be adapted to data acquisition very well when dolphins are in motion. Therefore, this study designs a novel, light-weighted, and portable EEG acquisition device aimed at relatively unrestricted EEG acquisition. An embedded main control board and an acquisition board were designed, and all modules are encapsulated in a 162 × 94 × 60 mm3 waterproof device box, which can be tied to the dolphin’s body by a silicon belt. The acquisition device uses customized suction cups with embedded electrodes and adopts a Bluetooth module for wireless communication with the ground station. The sampled signals are written to the memory card on board when the Bluetooth communication is blocked. A limited experiment was designed to verify the effectiveness of the device functionality onshore and underwater. However, more rigorous long-term tests on dolphins in various states with our device are expected in future to further prove its capability and study the movement-related artifacts.

Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.

Clinical Diagnosis of Psychiatry Based on Electroencephalography

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

Emotion Recognition of Single-electrode EEG based on Multi-feature Combination in Time-frequency Domain

Most researches on EEG emotion recognition are based on multi-electrode EEG signal acquisition equipment. There are few studies on single-electrode EEG equipment and the classification accuracy obtained is not ideal. In order to further improve the classification accuracy, the paper proposes an EEG emotion recognition method based on the combination of multiple features in the time-frequency domain. Firstly, the wavelet transform is used to decompose the EEG signals under the three types of emotion labels and extracts the time-frequency information of four frequency bands, including alpha, low beta, high beta, and gamma, and then uses different sizes of sliding time windows to calculate separately their statistical characteristics in the time-frequency domain. Finally, the Long Short-Term Memory (LSTM) network with time-frequency domain characteristics is used to extract the sequence information of the deep features and combine the output results of the softmax classifier. The results of experiments show that the average accuracy of the proposed method is 93.09% and 98.36% on emotion food and emotion state dataset, respectively. Compared with traditional machine learning methods and other deep learning methods, the time-frequency LSTM model named TF-LSTM in this paper has better EEG generalization ability and classification performance, and also provides a new feasible scheme for emotion recognition research based on single-electrode EEG signal.

Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.

Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving outcomes in cardiac arrest. Cerebral perfusion pressure (CePP) could represent cerebral circulation during CPR, but it is difficult to measure non-invasively. In this study, we developed the electroencephalogram (EEG) based brain index (EBRI) derived from EEG signals by machine learning techniques, which could estimate CePP accurately in a porcine cardiac arrest model. We conducted a randomised crossover study using nine female pigs. After 1min of untreated ventricular fibrillation, we performed CPR with 12 different 2-min tilting angle sessions, including two different head-up tilt (HUT) angles (30°, 15°) twice, horizontal angle (0°) four times and two different head-down tilt (HDT) angles (-15°, -30°) twice with the random order. We collected EEG signals using a single channel EEG electrode in real-time during CPR. We derived the EBRI models to predict the CePP classified by the 5 or 10 groups using three different machine learning algorithms, including the support vector machine (SVM), k-nearest neighbour (KNN) and random forest classification (RFC) method. We assessed the accuracy, sensitivity and specificity of each model. The accuracy of the EBRI model using an SVM algorithm in the 5-group CePP classification was 0.935 with a standard deviation (SD) from 0.923 to 0.946. The accuracy in the 10-group classification was 0.904 (SD: 0.896, 0.913). The accuracy of the EBRI using the KNN method in the 5-group classification was 0.927 (SD: 0.920, 0933) and in the 10-group was 0.894 (SD: 0.880, 0.907). The accuracy of the RFC algorithm was 0.947 (SD: 0.931, 0.963) in the 5-group classification and 0.920 (SD: 0.911, 0.929) in the 10-group classification. We developed the EBRI model using non-invasive acquisition of EEG signals to predict CePP during CPR. The accuracy the EBRI model was 0.935, 0.927 and 0.947 for each machine learning algorithm, and the EBRI could be used as a surrogate indicator for measuring cerebral perfusion during CPR.

EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers

Epilepsy is a neurobiological disease caused by abnormal electrical activity of the human brain. It is important to detect the epileptic seizures to help the epileptic patients. Using brain images for epilepsy diagnosis and seizure detection is time-consuming and complex process. Thus, electroencephalogram (EEG) signal analysis is focused in many papers of this field to detect the epileptic seizures. In addition, EEG signal acquisition is non-invasive and less painful for patients. However, raw EEG signal has many unrecognizable data not suitable for accurate diagnosis. Therefore, the raw EEG data must be analyzed while the features can be extracted. In this paper, discrete wavelet transform (DWT) is used to extract features of EEG signal by dividing it to five sub-bands. The proposed technique also includes genetic algorithm approach for selecting more effective features and finally, classification is performed by two strategies as artificial neural network (ANN) and support vector machine (SVM). The performance of two classifiers are compared where the simulation results show that the proposed strategy accuracy in detecting epilepsy seizures is better than other similar approaches in the literature.

Impedance and Noise of Passive and Active Dry EEG Electrodes: A Review

Dry electrodes are a promising solution for prolonged EEG signal acquisition, whereas wet electrodes may lose their signal quality in the same situation and require skin preparation for set-up. Here, we review the impedance and noise of passive and active dry EEG electrodes. In addition, we compare noise and input impedance of the EEG amplifiers. As there are multiple definitions of impedance in each EEG system, they are all first defined. Electrodes must be compatible with amplifiers to accurately record EEG signals. This implies that their impedance plays a significant role in amplifier compatibility and affects total input-referred noise. Therefore, we review the impedance and noise of state-of-the-art amplifiers and electrodes. Furthermore, we compare the various structures and materials used and their final impedance to that of wet electrodes. Finally, we compare state-of-the-art electrodes and amplifiers to the standards of the IFCN and IEC80601-2-26. We investigate bottlenecks and propose a guideline for future work on passive and active dry electrodes, as well as EEG amplifiers.

Wearable and Comfortable e-Textile Headband for Long-Term Acquisition of Forehead EEG Signals

Electroencephalography (EEG) has a wide range of applications in medical diagnosis, and novel form of Human Machine Interfaces (HMI) for controlling prosthetic implants, wheelchairs, and home appliances in various forms of paralysis. However, the current EEG setups are composed of many wires hanging down from the system, and individual electrodes that must be set manually, which is time-consuming. As a result, the overall system is neither comfortable, nor aesthetically appealing. Here, we introduce for the first time, a comfortable textile-based EEG headband system that is soft, conformal to the skin, and comfortable. We present materials and methods for fabrication of multi-layer stretchable e-textile, that interfaces the human epidermis from one side through printed electrodes, and interfaces a rigid PCB island on the second layer. We as well demonstrate a method that allows creation of VIAs (vertical interconnect access) between the layers, using a CO2 laser. All Electrodes are integrated into the headband, and thus there is no need for individual electrode placement, and individual wiring. By screen printing a home-made conductive stretchable ink, patient-specific EEG headbands can be tailor made considering the optimal positioning of the electrodes for each patient. We show that these printed electrodes benefit from a very low skin-electrode impedance, comparable to gold standard Ag/AgCl, or gold cup electrodes, thanks to the high surface area silver flakes used in this work. The e-textile headband interfaces with an EEG acquisition device that captures, amplifies, and transmits the data to an external mobile phone or a PC. Furthermore, the integrated amplification in the textile and the use of an EMF rejection layer on top of the electrodes were shown to reduce the unwanted EM noise that is picked up by the system. We as well show application of the developed headband for usage in Human Machine Interfaces and Sleep Data Acquisition. Altogether, this device is step toward wider use of EEG acquisition devices for daily-use applications.

Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method

In order to accurately and quantitatively describe the influence of a building’s spatial environment on subjective human perception, this research establishes a relationship framework between the spatial environment and subjective feelings, with the goal of improving occupant satisfaction and work efficiency. In pursuit of this goal, this study presents actual scenes through virtual space and introduces a new research concept for analyzing human brain data. The researchers adopted virtual reality technology in a controlled laboratory environment to compose building simulation spaces and created immersive space perceptions in response to different scenarios. Neural signal electroencephalogram (EEG) data were obtained in the simulation space from participants wearing EEG signal acquisition caps. The experiment process was divided into two phases: scene cognition and task performance. Changes in human perception, as measured on physiological, psychological, and work efficiency indexes, were examined in three environments: open natural, semi-open library, and closed basement spaces. Based on a 32-point analysis of the EEGs of 30 subjects, researchers determined four points and one region with the most significant EEG changes after scene switching. Also, by examining the EEG rhythms in the scene cognition experiment phase, the authors identified a coupling relationship between β rhythms and total time to task completion, proving the mechanistic relationship between β rhythms and work efficiency. Finally, this research revealed a correlation between subjective perception and physiological signals by analyzing the relevance of the connection between subjective questionnaire responses and the β rhythms demonstrated in the EEG experiment, and then deducing the mechanism affecting work efficiency as influenced by different environments. The results obtained show that in the context of changes to elements of a building’s spatial environment, human work efficiency is most related to the β rhythms at several test points and the right temporal lobe region of the brain. Moreover, β rhythms are closely related to satisfaction with human spatial perception. Therefore, this research provides a more accurate set of reference information for building space design based on occupant satisfaction and physical and mental health. The methods and conclusions demonstrated here can be adopted as feedback for ways of obtaining more realistic information from human brain signals and using those data to optimize architectural space design.

EEG BASED INTERNAL STRESS OBSERVATION: AN OVERVIEW OF METHODS INVOLVED FROM EEG ACQUISITION TO SYSTEM DESIGN

The design and development of smart sensors with signal processing tools and advanced algorithms has made possible the integration of a whole module as System in Package (SIP) to monitor many human problems and also provides a solution to reduce such problems. EEG plays an important role in electrophysiology field and is used to diagnose various human diseases related to brain. This paper presents the prospective study for developing an EEG sensor based human internal stress observation system. Here we reviewed the methodologies adopted for smart EEG signal acquisition from EEG sensor and have listed the desired features for stress identification based on cognitive changes of brain activity.

Development of a Hardware Circuit for Real-Time Acquisition of Brain Activity Using NI myDAQ

Electroencephalography (EEG) is the recording of electrical activity of the brain. The 10–20 system is the standard electrode location method used to acquire EEG data, which uses 21 electrodes to record the electrical activity of the brain. Patient preparation and correct electrode placement are important to obtain reliable outputs. The current 10–20 system consumes greater time for patient preparation and also causes discomfort due to a higher number of electrodes being used or wearing an uncomfortable cap. This paper focuses on reducing the number of electrodes, thus reducing patient discomfort as well as preparation time. Advancement in the field of hardware and software processing has led to the utilization of brain waves for communication between human and the computer. This work deals with EEG-based Brain–Machine Interface (BMI) intended for designing a portable single-channel EEG signal acquisition system. EEG signal was acquired using the data acquisition module [National Instruments (NI) myDAQ] and the signal was viewed in the NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) environment. It was observed that the peak-to-peak amplitude of alpha, beta and theta waves changes in accordance with the activity the subjects performed. Thus, the developed instrument was tested on 10 different subjects to acquire the alpha, beta and theta waves by performing different activities. From the results, it can be concluded that the developed system can be used for studying a person’s brain waves (alpha, beta and theta) based on the activity performed by the subject with a limited number of electrodes.

A brain-computer interface system for smart home control based on single trial motor imagery EEG

In recent years, researches on brain signal recognition and brain-computer interface control have made great progress. By analysing electroencephalogram (EEG), a specific brain activity can be detected and the signal can be used to control smart devices and help people to complete difficult and complicated tasks, especially for people with disabilities. This paper presents the design and implementation of a novel brain-computer interface system for smart home control using single trial motor imagery EEG. The system adopts STM32 Microcontroller Unit (MCU) and ThinkGear Asic Module (TGAM) to realise the acquisition and recognition of EEG signals. It can transfer the signals to portable devices through Bluetooth modules. Three main EEG features including Alpha, Beta, and Gamma waves are discussed. It is tested during simple actions such as blinking in various situations. The experimental results show that the implemented system is suitable for extracting specific EEG signals to control smart home devices.