Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions
17 Jul 2017-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 169, Iss: 3, pp 12-16
TL;DR: This paper aims to present an overview on BCI different EEG brain signal recording artifacts and the methodologies to remove these artifacts from the signal focusing on different novel trends at BCI research areas.
Abstract: Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded signal during the BCI preprocessing stage of to achieve preferable results at the learning stage. This paper aims to present an overview on BCI different EEG brain signal recording artifacts and the methodologies to remove these artifacts from the signal focusing on different novel trends at BCI research areas.
Citations
More filters
TL;DR: Genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals and it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.
Abstract: In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.
78 citations
Cites background from "Brain Computer Interface: EEG Signa..."
...According to [4], EEG is non-invasive and has the advantages of relatively easy signal acquisition and high time resolution....
[...]
TL;DR: This study investigates the feasibility of identifying construction hazards by developing an EEG classifier based on the experiments conducted in an immersive virtual reality (VR) environment and shows that the CatBoost classifier achieved the highest performance with 95.1% accuracy.
34 citations
TL;DR: The deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-Solving methods in their future pursuit of strategic organizational goals.
Abstract: Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.
29 citations
TL;DR: A multi-domain hybrid feature pool is designed to identify most of the important information from the signal and the k-nearest neighbor (k-NN) algorithm is used for final classification.
Abstract: Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet-based bandwidth specific feature analysis from the time-frequency domain. Then, a wrapper-based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the non-redundant features. Finally, the k-nearest neighbor (k-NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non-linear dimensionality reduction techniques, as well as those without feature ranking.
27 citations
Cites background from "Brain Computer Interface: EEG Signa..."
...It measures signals from the scalp rather than the brain itself [8]....
[...]
TL;DR: A deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features and could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors.
Abstract: Brain–computer interface (BCI) is a powerful system for communicating between the brain and outside world. Traditional BCI systems work based on electroencephalogram (EEG) signals only. Recently, researchers have used a combination of EEG signals with other signals to improve the performance of BCI systems. Among these signals, the combination of EEG with functional near-infrared spectroscopy (fNIRS) has achieved favourable results. In most studies, only EEGs or fNIRs have been considered as chain-like sequences, and do not consider complex correlations between adjacent signals, neither in time nor channel location. In this study, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features. The proposed model incorporates the spatial relationship between EEG and fNIRS signals. This could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors. The tests show that the proposed model has a precision of 99.6%.
26 citations
References
More filters
TL;DR: FieldTrip is an open source software package that is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data.
Abstract: This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
7,963 citations
"Brain Computer Interface: EEG Signa..." refers methods in this paper
...There are several techniques for brain signal acquisition: invasive, semiinvasive, and non-invasive [10, 19]....
[...]
TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
6,803 citations
TL;DR: The brain's electrical signals enable people without muscle control to physically interact with the world through the use of their brains' electrical signals.
Abstract: The brain's electrical signals enable people without muscle control to physically interact with the world.
2,361 citations
"Brain Computer Interface: EEG Signa..." refers background in this paper
...Spatial filtering concept is to use a small number of new channels that are a linear combination of the original channels of EEG brain signal reading [20]....
[...]
...Physiologic artifacts occurred due to the human body, and arose from sources other than the brain such as (eye blink, eyeball movement, breath, heart beats, muscles movement) [20]....
[...]
...Figure 2 illustrates the BCI system stages and its components [20]....
[...]
TL;DR: The possibility that other cognitive tasks, including those used in imaging studies, may prove to be more effective than motor imagery has been the most commonly used task is explored.
425 citations
"Brain Computer Interface: EEG Signa..." refers background in this paper
...BCI research and development has focused primarily on neuro-prosthetics applications that aim to restore damaged hearing, sight and movement [1]....
[...]
...Nowadays, BCI research reached remarkable results at robot control [6, 8, 9, 12], motor disabilities recovery using BCI chip implants [1, 4, 5, 7, 11], medical diagnoses and prediction [15, 18], security and authentications [14] and game controlling [10, 16, 17]....
[...]
TL;DR: The application areas that could benefit from brain waves in facilitating or achieving their goals are shown and major usability and technical challenges that face brain signals utilization in various components of BCI system are discussed.
397 citations
"Brain Computer Interface: EEG Signa..." refers background or methods in this paper
...Nowadays, BCI research reached remarkable results at robot control [6, 8, 9, 12], motor disabilities recovery using BCI chip implants [1, 4, 5, 7, 11], medical diagnoses and prediction [15, 18], security and authentications [14] and game controlling [10, 16, 17]....
[...]
...There are several techniques for brain signal acquisition: invasive, semiinvasive, and non-invasive [10, 19]....
[...]