Evaluation of Density Based Odor Classification by General Type-2 Fuzzy Set Induced Pattern Classifier

Laha, Mousumi; Ghosh, Lidia; Parui, Sricheta; Ghosh, Sayantani; Konar, Amit

doi:10.1109/wispnet.2018.8538634

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Among existing neuroimaging modalities, electroencephalogram (EEG) is the most commonly used technique to measure and record brain activity during olfactory perception due to its advantages of noninvasiveness, fine temporal resolution, and portability [6,7]. At present, various classification systems based on olfactory EEG signals have been developed to recognize odor categories [8][9][10][11], odor-induced pleasantness [5,[12][13][14][15], and odor concentrations [16][17][18]. Most previous studies adopt EEG acquisition devices with many channels or electrodes to collect olfactory EEG data [7, 9-14, 17, 18].…”

Section: Introductionmentioning

confidence: 99%

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

2022

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Objective. The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels. Approach. In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry (RG) classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search (BHS) algorithm, including an opposition-based learning strategy (OBL) for generating high-quality initial population, an adaptive parameter strategy (APS) for improving search capability, and a bitwise operation strategy (BOS) for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels. Main results. With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy. Significance. The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.

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Section: Introductionmentioning

confidence: 99%

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

2022

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“…Zhang et al (2019) presented that channel-frequency convolutional neural network yields best accuracy of 68.79% in gamma band using power spectrum density and differential entropy features using 13 odor stimuli [8]. Laha et al (2018) evaluated concentration of odors using general type-2 fuzzy set for odor classification. According to their work, higher density of odor yields higher classification performance [9].…”

Section: Introductionmentioning

confidence: 99%

“…Laha et al (2018) evaluated concentration of odors using general type-2 fuzzy set for odor classification. According to their work, higher density of odor yields higher classification performance [9]. Becerra et al (2018) proposed and odor identification system within 5 sub-bands.…”

Section: Introductionmentioning

confidence: 99%

Eeg Channel Selection Using Differantial Evolution Algorithm and Particle Swarm Optimization for Classification of Odorant-Stimulated Records

Şeker¹,

Özerdem²

2021

European Journal of Technic

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A Brain to UAV Communication Model using Stacked Ensemble CSP algorithm based on Motor Imagery EEG signal

Parui

Basu

Ghosh

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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Evaluation of Density Based Odor Classification by General Type-2 Fuzzy Set Induced Pattern Classifier

Cited by 8 publications

References 16 publications

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

Eeg Channel Selection Using Differantial Evolution Algorithm and Particle Swarm Optimization for Classification of Odorant-Stimulated Records

A Brain to UAV Communication Model using Stacked Ensemble CSP algorithm based on Motor Imagery EEG signal

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