Correction to: Review of Machine Learning Techniques for EEG Based Brain Computer Interface

Aggarwal, Swati; Chugh, Nupur

doi:10.1007/s11831-021-09703-6

Cited by 4 publications

(5 citation statements)

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“…As deep learning has been developed and improved, it has been applied to various fields and is widely used in EEG ( Altaheri et al, 2021 ; Rahman et al, 2021 ; Aggarwal and Chugh, 2022 ). In several previous studies, MRF-CNN performed better classification of multiple images and signal data than other SRF-CNNs, as shown by analyzing numerous datasets ( Hu et al, 2019 ; Dai et al, 2020 ; Liu et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network

Park

Lee

2023

Front. Hum. Neurosci.

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IntroductionIn this study, we classified electroencephalography (EEG) data of imagined speech using signal decomposition and multireceptive convolutional neural network. The imagined speech EEG with five vowels /a/, /e/, /i/, /o/, and /u/, and mute (rest) sounds were obtained from ten study participants.Materials and methodsFirst, two different signal decomposition methods were applied for comparison: noise-assisted multivariate empirical mode decomposition and wavelet packet decomposition. Six statistical features were calculated from the decomposed eight sub-frequency bands EEG. Next, all features obtained from each channel of the trial were vectorized and used as the input vector of classifiers. Lastly, EEG was classified using multireceptive field convolutional neural network and several other classifiers for comparison.ResultsWe achieved an average classification rate of 73.09 and up to 80.41% in a multiclass (six classes) setup (Chance: 16.67%). In comparison with various other classifiers, significant improvements for other classifiers were achieved (p-value < 0.05). From the frequency sub-band analysis, high-frequency band regions and the lowest-frequency band region contain more information about imagined vowel EEG data. The misclassification and classification rate of each vowel imaginary EEG was analyzed through a confusion matrix.DiscussionImagined speech EEG can be classified successfully using the proposed signal decomposition method and a convolutional neural network. The proposed classification method for imagined speech EEG can contribute to developing a practical imagined speech-based brain-computer interfaces system.

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Section: Discussionmentioning

confidence: 99%

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network

Park

Lee

2023

Front. Hum. Neurosci.

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“…where A refers to the PSD of interest and R refers to the reference PSD. The SMR are most prominent in the ranges of Beta (18)(19)(20)(21)(22)(23)(24)(25)(26) and Mu (8)(9)(10)(11)(12). They are also clearly observable in the locations of the C4 and C3 electrodes [70].…”

Section: � Sensorimotor Rhythmsmentioning

confidence: 91%

“…EEG signals have a frequency range of 0.5-50 Hz, and they are a reflection of an individual's brainwave activity. The frequencies can be further categorised into the following bands: Gamma (30-50 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), Alpha (8)(9)(10)(11)(12), Theta (4-8 Hz), and Delta (0.5-4 Hz). Using a variety of signal processing techniques and feature extraction methods, we are able to better understand the nature of these signals.…”

Section: Introductionmentioning

confidence: 99%

“…Physiological signals such as EEG and EMG are complex in nature, and we need to utilise a variety of techniques in the areas of signal processing and feature extraction to study them effectively [4]. Machine learning techniques have been shown to be effective in the classification of such signals when used together with a variety of signal processing techniques for feature extraction [8][9][10][11][12][13]. Recent deep learning techniques are able to achieve high accuracy but mainly require more complex architectures, affecting the power consumption and real-time capability.…”

Section: Introductionmentioning

confidence: 99%

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BIO‐inspired fuzzy inference system—For physiological signal analysis

Suppiah

Kim

Abidi

et al. 2023

IET Cyber-Syst and Robotics

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When a person's neuromuscular system is affected by an injury or disease, Activities‐for‐Daily‐Living (ADL), such as gripping, turning, and walking, are impaired. Electroencephalography (EEG) and Electromyography (EMG) are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject, and they are used in Brain–Computer Interface (BCI) or robotic rehabilitation systems. However, existing BCI or robotic rehabilitation systems use signal classification technique limitations such as (1) missing temporal correlation of the EEG and EMG signals in the entire window and (2) overlooking the interrelationship between different sensors in the system. Furthermore, typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions; (3) their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals. A novel classification model, named BIOFIS is proposed, which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships. It explores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory (LSTM) block. The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward, grip and raise, lower and release, and reverse. The system can achieve 98.6% accuracy for a 4‐way action using EEG data and 97.18% accuracy using EMG data. Moreover, even without the dominant signal, the accuracy scores were 90.1% for the EEG data and 85.2% for the EMG data. The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

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“…The greater the Information Gain value of a term, the more significant the feature is. Meanwhile, to measure how often a word appears in a document, and count the number of documents in which the word appears, the Term Frequency-Inverse Document Frequency (TF-IDF) word weighting is used [8].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

Fatah,

Rochman,

Setiawan

et al. 2024

E3S Web Conf.

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Sentiment analysis is natural language processing (NLP) that uses text analysis to recognize and extract opinions in text. Analysis is used to convert unstructured information into more structured information, also to determine whether an object has a positive, negative, or neutral tendency, and is an effort to facilitate decision making for tourism managers as a recommendation in developing tourist attractions. In this study, opinions were conducted on tourism reviews in Bangkalan using the Naïve Bayes method. This method is a machine learning algorithm to classify text into concepts that are easy to understand and provide accurate results with high efficiency. This method is proven to provide excellent results with a high level of accuracy, especially for large data, but has some drawbacks, sensitive to feature selection. Thus, a feature selection process is needed to improve classification efficiency by reducing the amount of data analyzed, with the Information Gain feature selection method. The word weighting method uses TF-IDF, while the data used comes from google maps reviews taken through web scraping, where tourist visitors provide reviews and ratings of places that have been visited. However, the large number of reviews can make it difficult for tourist attractions managers to manage them, so the process of labeling the sentiment class of the review data obtained 3649 reviews, with 2583 positive, 275 negative, and 457 neutral. Based on the test results that have been carried out using the Information Gain threshold of 0.0001, 0.0003, and 0.0007 can improve the accuracy of the Naïve Bayes model, for the best test at threshold 0.0007, with an accuracy value of 78.68%, precision 80.44%, recall 82.59%, and f1-score 82.53%, from the test results it shows that the use of information gain feature selection and SMOTE technique has a fairly good performance in classifying public opinion sentiment data on tourism in Bangkalan Regency, meaning that tourism management is good seen from the results of visitor satisfaction sentiment.

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Correction to: Review of Machine Learning Techniques for EEG Based Brain Computer Interface

Cited by 4 publications

References 0 publications

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network

BIO‐inspired fuzzy inference system—For physiological signal analysis

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

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