Classification Algorithms for EEG-Based Brain-Computer Interface

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

EEGG: An Analytic Brain-computer Interface Algorithm

10.36227/techrxiv.13516145.v3 ◽

2021 ◽

Author(s):

Gang Liu ◽

Jing Wang

Keyword(s):

Brain Activity ◽

Brain Regions ◽

Classification Performance ◽

Data Driven ◽

Computer Interface ◽

Eeg Signals ◽

Eeg Data ◽

Data Driven Approach ◽

Left And Right ◽

<div><div> <p><a></a></p><div> <p><a></a><a><i>Objective. </i></a>Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEGBCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. </p> <p><i>Approach. </i>Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relationship frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG’s classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCIbased analysis of the brain. </p> <p><i>Main results. </i>(1) EEGG was more robust than typical “CSP+” algorithms for the poorquality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. </p> <p><i>Significance. </i>EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (<i>in analogy with the data-driven but human-readable Fourier transform and frequency spectrum</i>), which offers a novel frame for analysis of the brain.</p> </div> </div></div><div><p></p></div>

Application of Convolutional Neural Network Method in Brain Computer Interface

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012044 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012044

Author(s):

Lingzhi Chen ◽

Wei Deng ◽

Chunjin Ji

Keyword(s):

Pattern Recognition ◽

Recognition Accuracy ◽

Computer Interface ◽

Eeg Signals ◽

Network Method ◽

Electroencephalogram Eeg ◽

The Brain ◽

Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2011.3.2126 ◽

2011 ◽

Vol 6 (3) ◽

pp. 403 ◽

Cited By ~ 1

Author(s):

Ioan Dzitac ◽

Tiberiu Vesselényi ◽

Radu Cătălin Ţarcă

Keyword(s):

Fuzzy Inference ◽

Brain Activity ◽

Computer Interface ◽

Eeg Signals ◽

Inference System ◽

Eeg Data ◽

Recent Developments ◽

Potential Activity ◽

Inference Systems

A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography - EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v7i3.4621 ◽

2020 ◽

Author(s):

Jafar Zamani ◽

Ali Boniadi Naieni

Keyword(s):

Feature Extraction ◽

Brain Activity ◽

Support Vector ◽

High Temporal Resolution ◽

Discrete Wavelet ◽

Eeg Signal ◽

Eeg Signals ◽

High Efficient ◽

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

Deep learning for classification of sleep EEG data during the epidemic of Coronavirus Disease

Biotechnology and Bioprocessing ◽

10.31579/2766-2314/010 ◽

2020 ◽

Vol 1 (2) ◽

pp. 01-05

Author(s):

Bin Zhao

Keyword(s):

The Body ◽

Sleep Eeg ◽

Quality Of Sleep ◽

Eeg Signals ◽

Eeg Data ◽

The Impact ◽

Electroencephalogram Eeg ◽

Important Basis

Sleep is an important part of the body's recuperation and energy accumulation, and the quality of sleep also has a significant impact on people's physical and mental state during the epidemic of Coronavirus Disease. It has attracted increasing attention how to improve the quality of sleep and reduce the impact of sleep related diseases on health. The electroencephalogram (EEG) signals collected during sleep belong to spontaneous EEG signals. Spontaneous sleep EEG signals can reflect the body own changes, which is also an important basis for diagnosis and treatment of related diseases. Therefore, the establishment of an effective model for classifying sleep EEG signals is an important auxiliary tool for evaluating sleep.

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

International Journal of Neural Systems ◽

10.1142/s0129065717500642 ◽

2018 ◽

Vol 28 (06) ◽

pp. 1750064 ◽

Cited By ~ 13

Author(s):

Vitaly Schetinin ◽

Livija Jakaite ◽

Ndifreke Nyah ◽

Dusica Novakovic ◽

Wojtek Krzanowski

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Identification Accuracy ◽

Group Method ◽

Autoregressive Modeling ◽

Eeg Signals ◽

Person Identification ◽

Benchmark Data ◽

Eeg Data ◽

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2011.3.2125 ◽

2011 ◽

Vol 6 (3) ◽

pp. 403 ◽

Cited By ~ 6

Author(s):

Ioan Dzitac ◽

Tiberiu Vesselényi ◽

Radu Cătălin Ţarcă

Keyword(s):

Fuzzy Inference ◽

Brain Activity ◽

Computer Interface ◽

Eeg Signals ◽

Inference System ◽

Eeg Data ◽

Recent Developments ◽

Potential Activity ◽

Inference Systems

Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

Procedia of Engineering and Life Science ◽

10.21070/pels.v1i1.750 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Maulana Angga Pribadi

Keyword(s):

Signal Analysis ◽

Nearest Neighbor ◽

Brain Activity ◽

Discrete Wavelet ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Epilepsy Classification ◽

Non Stationary Signal ◽

Epileps is a disorder of the contents of the nervous system of the human brain resulting in the presence of abnormal activity that is the excessive activity of neuron cells in the brain. In Indonesia there are more than 1,400,000 cases of Epilepsy each year with 70,000 additional cases each year. About 4050% occurs in children. A widely used method for assessing brain activity is through a sephalogram (EEG) Electrone signal. The Epilepsy classification system is built with extraction and identifikas stages. Wavelet exctraction is suitable for non-stationary signal analysis such as EEG signals. Wavelet tranformation can extract signal components only at the required frequency. So that it can also reduce the amount of data but without losing meaningful information. But to make it work and can be used on a system needs to be done classification in order to be able to distinguish between commands from each other. So it is used K-Nearest Neighbour (K-NN) classification method so that the signal that has been eliminated buzz can be directly entered into the classification to determine the correct wrongness of a data. In this study obtained the results of data accuracy value that K = 1 has the largest percent of 100% and the smallest percent is found in K = 7 and K = 11 namely 14.2% and 18.2% it is caused by the presence of classes that do not match the test data so as to reduce the percentage of accuracy in the K.

A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting

Bioengineering ◽

10.3390/bioengineering8020021 ◽

2021 ◽

Vol 8 (2) ◽

pp. 21

Author(s):

Andrea Valenti ◽

Michele Barsotti ◽

Davide Bacciu ◽

Luca Ascari

Keyword(s):

Real Time ◽

Deep Neural Networks ◽

State Of The Art ◽

Brain Activity ◽

Computer Interface ◽

Non Invasive ◽

The Brain ◽

Movement Intention

Decoding motor intentions from non-invasive brain activity monitoring is one of the most challenging aspects in the Brain Computer Interface (BCI) field. This is especially true in online settings, where classification must be performed in real-time, contextually with the user’s movements. In this work, we use a topology-preserving input representation, which is fed to a novel combination of 3D-convolutional and recurrent deep neural networks, capable of performing multi-class continual classification of subjects’ movement intentions. Our model is able to achieve a higher accuracy than a related state-of-the-art model from literature, despite being trained in a much more restrictive setting and using only a simple form of input signal preprocessing. The results suggest that deep learning models are well suited for deployment in challenging real-time BCI applications such as movement intention recognition.

Complexity Measures of Brain Electrophysiological Activity

Journal of Psychophysiology ◽

10.1027/0269-8803/a000024 ◽

2010 ◽

Vol 24 (2) ◽

pp. 131-135 ◽

Cited By ~ 5

Author(s):

Włodzimierz Klonowski ◽

Pawel Stepien ◽

Robert Stepien

Keyword(s):

Brain Activity ◽

Deterministic Chaos ◽

Epileptic Seizures ◽

Eeg Signal ◽

Eeg Signals ◽

Complexity Measures ◽

Electrophysiological Activity ◽

Signal Complexity ◽

Physiological Sleep ◽

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.