EEG-Based Brain-Computer Interfacing (BCI)

Bansal, Dipali; Mahajan, Rashima

doi:10.1016/b978-0-12-814687-3.00002-8

Cited by 20 publications

(14 citation statements)

References 131 publications

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“…It is more effective in collecting these signals from internal measurement, since the measured values are more accurate. The externally measured signals are of comparatively lower quality when compared to the internally measured values [2]. However, internal measurements are a potentially dangerous technique, since the measuring devices have to be surgically placed inside the body, leading to higher chances of life threat and, hence, it is also known as an invasive method.…”

Section: Introductionmentioning

confidence: 99%

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Jusas

Samuvel

2019

Applied Sciences

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The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher′s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher′s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

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Section: Introductionmentioning

confidence: 99%

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Jusas

Samuvel

2019

Applied Sciences

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“…In many of studies, it has been claimed that some features that are associated with the non-linear dynamics of the EEG signals are useful to represent the characteristics of these signals in different situations [27][28][29][30]. In addition to the importance of the speed and accuracy of the system response, the other challenge is reducing the number of EEG channels.…”

Section: Non-linear Featuresmentioning

confidence: 99%

Detection of static, dynamic, and no tactile friction based on nonlinear dynamics of EEG signals: A preliminary study

Baghdadi

Amiri

2021

Chaos, Solitons & Fractals

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Touching an object leads to a frictional interaction between the skin and the object. There are two kinds of friction: the first contact that leads to static friction and the dragging phase that leads to dynamic friction. No study has been performed to show the effect of friction type on EEG signals.The main goal of the current study is to investigate the effect of tactile friction on non-linear features of EEG signals.Participants performed a tactile task that each of its trials had three states: the sensation of 1) static friction, 2) dynamic friction, and 3) no friction. During the experiment, EEG signals were recorded, and different linear and non-linear EEG indices were extracted and analyzed to find the effect of the tactile friction on EEG signals.Linear features such as spectral features were not a good choice to distinguish between the states. However, non-linear features such as Lyapunov exponent, Higuchi's dimension, and Hurst exponent had the potential to separate the mentioned states. Results also showed signs of predictability (negative Lyapunov exponent) in the signals recorded during dynamic friction and the existence of long-range dependency (memory) in EEG signals recorded during all states. The complexity of the tactile system in Theta band was also higher than the Delta band.The results of this research not only increase our knowledge about brain non-linear dynamics in response to tactile friction but also lead to a design of a preliminary system that can automatically detect friction between the skin and surfaces.

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“…• Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30): The beta waves are a sign of actively engaged state of mind. These waves dominate a general day for a human.…”

Section: Related Workmentioning

confidence: 99%

“…-P300 ERPs -SCPs -SMRs In SCPs patient can control positive and negative thoughts through broad preparing and the associated BCI system deciphers these progressions to move or control things on a PC screen. Along these lines, brain-disabled individuals are encouraged in sharing their thoughts and perspectives by the utilization of spelling gadgets [21], thought interpretation gadgets [22], and for fundamental and verbal correspondence too. [ [27] • Assistive Devices for Locomotion: Autonomous movement proves to be significant as routine exercises in the world of disabled and differently-abled people.…”

Section: A Clinical Applicationsmentioning

confidence: 99%

Electroencephalogram based Brain Computer Interface System Analysis

2020

IJITEE

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Electroencephalogram is the study of electrical signals of the brain recorded using a mesh like structure containing electrodes that is placed on the scalp. The history of EEG dates more than a century back, when the brains of rabbits and monkeys were studied and almost 50 years later in the year 1924, the first ever brain activity of human was noted by famed psychiatrist and physiologist Hans Berger from Germany. EEG based systems that can communicate with the brain are categorized as Brain Computer Interface i.e. BCI. The electrodes read the brain signals, amplifies them in order to be studied more accurately by the machine send them to machine after converting it into digital form. With the recent changes in technology better electrodes are being used which can catch highly sensitive signals as well.[1][2] EEG based BCI systems can change the world for many people as it holds so much power if thought properly. This paper reviews the EEG functioning and some innovations in BCI and also proposes ideas about potential help for people suffering from hearing and speech impairment.

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EEG-Based Brain-Computer Interfacing (BCI)

Cited by 20 publications

References 131 publications

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Detection of static, dynamic, and no tactile friction based on nonlinear dynamics of EEG signals: A preliminary study

Electroencephalogram based Brain Computer Interface System Analysis

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