EEG Multiclass Signal Classification Based on Subtractive Clustering-ANFIS and Wavelet Packet Decomposition

Riyadi, Munawar Agus; Setiawan, Iwan; Amir, Afandi

doi:10.1109/ieit53149.2021.9587407

Cited by 10 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 6 presents a summary of the publicly available datasets, which will be described in details in the following subsections. Considering the other papers, notice that the experimental paradigms usually concern the motor imagination of left/right hand/fist [82,86,[89][90][91][92]95,97,106,108,111,115,116,119,[124][125][126][127]130,131,133,134,[137][138][139][142][143][144][145][146][147]149,150,154,157,159,161,179], both hands [95,115,116,126,138,161], dominant or single hand movements [83,98,109,…”

Section: Dataset and Experimental Paradigmsmentioning

confidence: 99%

“…In the temporal domain, statistical features such as the standard deviation, skewness, kurtosis, entropy, and energy are considered [85,137,179].…”

mentioning

confidence: 99%

“…To reduce the data dimensionality the traditional channel set C{3,4,z}, located over the central cortical area, are often considered [88,143,144,154,156,163]. However, in several cases, the choice of the electrodes mainly depends on the used device [82,105,108,137,140]. Electrodes positioned over the fronto-central and central-parietal areas are also frequently taken into account.…”

mentioning

confidence: 99%

See 2 more Smart Citations

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

View full text Add to dashboard Cite

In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

show abstract

Section: Dataset and Experimental Paradigmsmentioning

confidence: 99%

“…In the temporal domain, statistical features such as the standard deviation, skewness, kurtosis, entropy, and energy are considered [85,137,179].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…At the same time, a high-pass filter h is utilized to deconstruct the signal. The approximation coefficients and detail coefficients (from the high-pass filter) are outputs (from the lowpass) [23]. The fact that the two filters are connected is crucial, as they are referred to as a quadrature's mirrors filter.…”

Section: Classification Process By Optimized Qnnmentioning

confidence: 99%

Classification of EEG Signal by Using Optimized Quantum Neural Network

Abdul-Zahra

Jawad

Gheni³

et al. 2021

IJEEI

View full text Add to dashboard Cite

In recent years the algorithms of machine learning were used for brain signals identification as a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals. The wavelet transform (WT) and the independent component analysis (ICA) were utilized for feature extraction. These algorithms were used to reduce the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize the EEG signals.The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized QNN provided us with somewhat faster and more realistic results. According to simulation results, the total classification for ICA is 82.4 percent, while the total classification for WT is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT.

show abstract

“…This research aims to develop a system that is capable of processing voice input for the recognition of Al-Quran reading in the form of Tajwid pronunciation, using wavelet signal extraction and classification of Tajweed rules using a Wavelet Packet Adaptive Network Based on Fuzzy Inference Systems. Some of the previous research, specifically on the analysis of each, has provided a sharp analysis of the Wavelet Packet Adaptive Network [14][15][16][17][18] and the application of the ANFIS method [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Identification of Tajweed Recognition using Wavelet Packet Adaptive Network based on Fuzzy Inference Systems (WPANFIS)

Siregar,

Satria,

Prayogi

et al. 2024

IOTA

View full text Add to dashboard Cite

This research aims to develop a system capable of processing voice input to recognize Al-Quran reading by recitation of Tajwid, using wavelet signal extraction and classification of Tajwid rules using ANFIS. The process stages include data acquisition, audio data pre-processing, extraction using wavelet packets, division of training data and test data, and classification. The data obtained were 20 observations from 10 observations carried out in data pre-processing. The wavelet decomposition process produces six main features as ANFIS input variables and 64 rules. Then the data was separated into 17 observations for training data and three for testing data. The test results obtained from the training that had been carried out produced plots that were too fit; in this experiment, the WPANFIS classification model got 100% appropriate classification and SSE values that were the same as the training result, 0.00081225.

show abstract

EEG Multiclass Signal Classification Based on Subtractive Clustering-ANFIS and Wavelet Packet Decomposition

Cited by 10 publications

References 18 publications

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Classification of EEG Signal by Using Optimized Quantum Neural Network

Identification of Tajweed Recognition using Wavelet Packet Adaptive Network based on Fuzzy Inference Systems (WPANFIS)

Contact Info

Product

Resources

About