Review of Machine Learning Techniques for EEG Based Brain Computer Interface

Aggarwal, Swati; Chugh, Nupur

doi:10.1007/s11831-021-09684-6

Cited by 115 publications

(61 citation statements)

References 113 publications

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“…The dependent brain-computer interface enables people to use some form of motion control, such as gaze. The brain-computer interface based on moving images is one of the most commonly-used paradigms of brain-computer interface [ 79 ]. Independent BCIs such as P300 evoked potentials, steady-state visual evoked potentials (SSVEPs), sensorimotor rhythms, motion-onset visual evoked potentials, and slow cortical potentials can be utilized to extract control signals; SSVEPs are periodic evoked potentials (PEPs) generated by rapidly repeating visual stimulation, particularly at a frequency greater than 6 Hz.…”

Section: Eeg Control Strategiesmentioning

confidence: 99%

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

et al. 2022

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Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.

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Section: Eeg Control Strategiesmentioning

confidence: 99%

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

et al. 2022

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“…There is significant research in the area of EEG, exploring how the human brain works [17] [18] [19] [20]. It has been generalised to four different frequency bands of signals that occur in the brain: (i) delta, (ii) theta, (iii) alpha, and (iv) beta.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of EEG-Mechatronic System Interface for Computational Intelligence

Aume¹,

Pal²,

Mukhopadhyay³

2022

Preprint

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The devices that can read Electroencephalography (EEG) signals have been widely used for Brain-Computer Interfaces (BCIs). Popularity in the field of BCIs has increased in recent years with the development of several consumergrade EEG devices that can detect human cognitive states in real-time and deliver feedback to enhance human performance. Several studies are conducted to understand the fundamentals and essential aspects of EEG in BCIs. However, the significant issue of how can consumer-grade EEG devices be used to control mechatronic systems effectively has been given less attention. In this paper, we have designed and implemented an EEG BCI system using the Open-BCI Cyton headset and a user interface running a game. We employ real-world participants to play a game to gather training data that was later put into multiple machine learning models, including a linear discriminant analysis (LDA), k-nearest neighbours (KNN), and a convolutional neural network (CNN). After training the machine learning models, a validation phase of the experiment took place where participants tried to play the same game but without direct control, utilising the outputs of the machine learning models to determine how the game moved. We find that a CNN trained to the specific user playing the game performed with the highest activation accuracy from the machine learning models tested, allowing for future implementation with a mechatronic system.

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“…EEG tabanlı BBA sistemleri kullanılarak, bilgisayar faresi (McFarland, Krusienski, Sarnacki, & Wolpaw, 2008), robotik kol (Cao vd., 2021), tekerlekli sandalye (Palumbo, Gramigna, Calabrese, & Ielpo, 2021) hecelemeye (Yin, Zhou, Jiang, Yu, & Hu, 2014), oyun (Li vd., 2021) ve hatta güvenlik sistemlerine (Ahmad & Ahuja, 2022) kadar birçok uygulama geliştirilmiştir. BBA sistemlerinin en sık kullanıldığı alanlardan biri de heceleme sistemleridir (Aggarwal & Chugh, 2022). Heceleme sistemleri, bireylerin bilgisayar ekranında bulunan sanal bir klavyeyle düşüncelerini yazarak etrafındaki kişilere aktarmalarını sağlayan en güncel BBA uygulamalarından biridir.…”

Section: Introductionunclassified

Investigation of the Effects of Two and Three Dimensional Paradigms on Event-Related Potentials Using Wavelet Transform Based Method

Korkmaz

2022

EJOSAT

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In event-related P300 potential-based Brain Computer Interface (BCI) systems, the main focus of the studies is how different stimulus types affect system performance. In a study, a data set was created using classical two-dimensional row-column flashing-based and proposed three-dimensional column flashing-based paradigms. According to the results obtained, the proposed three-dimensional column flashing shows high performance in terms of classification accuracy of stimulus presentation. However, how this paradigm changes the P300 potential has not been demonstrated. In this study, the effect of both paradigms on the event-related P300 potential is discussed using a wavelet transform-based method in terms of both time and frequency space. According to the results obtained, it was observed that the proposed paradigm activated more frequency bands on the P300 potential. In addition, using the proposed method, higher P300 amplitude was obtained in many channels. As a result, more effective P300 signals are received in stimulus presentation using the proposed paradigm, increasing the BCI system performance.

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Review of Machine Learning Techniques for EEG Based Brain Computer Interface

Cited by 115 publications

References 113 publications

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

Design and Implementation of EEG-Mechatronic System Interface for Computational Intelligence

Investigation of the Effects of Two and Three Dimensional Paradigms on Event-Related Potentials Using Wavelet Transform Based Method

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