Decoding olfactory EEG signals for different odor stimuli identification using wavelet-spatial domain feature

Zhang, Xiao-Nei; Meng, Qing-Hao; Zeng, Ming; Hou, Huayan

doi:10.1016/j.jneumeth.2021.109355

Cited by 14 publications

(9 citation statements)

References 37 publications

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“…delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (14-30 Hz) and gamma . Previous works [7,11] have reported that compared with other frequency bands (i.e. delta, theta, alpha, and beta bands), the gamma band is more related to olfaction.…”

Section: Eeg Data Preprocessingmentioning

confidence: 92%

“…Then, the acquired multi-channel EEG signals are band-pass filtered by a Butterworth filter to enhance the signal-to-noise ratio. Since the gamma oscillation of brain activity is highly associated with olfaction [7,11], EEG signals are further filtered to a specific gamma band . Afterwards, the preprocessed data are divided into training and test sets using a cross validation (CV) technique.…”

Section: Methodsmentioning

confidence: 99%

“…Next, for Dataset 1, the filtered data were down-sampled to 250 Hz. For Dataset 2, the filtered data were down-sampled to 256 Hz that was the same as the previous study [11]. Lastly, for both datasets, the multi-channel EEG signals after down-sampling were further filtered into five main physiological EEG rhythms, i.e.…”

Section: Eeg Data Preprocessingmentioning

confidence: 99%

“…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%

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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: Eeg Data Preprocessingmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Eeg Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…Indeed, in [ 40 ], it is shown that the theta frequency band has statistically significant reduction, in agreement with previous studies [ 41 , 42 , 43 ]. On the other hand, more recent studies based on time–frequency analysis of the EEG signal consider the gamma band as being associated with smell [ 44 , 45 ].…”

Section: Olfactory Event Related Potentialsmentioning

confidence: 99%

Assessment and Scientific Progresses in the Analysis of Olfactory Evoked Potentials

et al. 2022

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The human sense of smell is important for many vital functions, but with the current state of the art, there is a lack of objective and non-invasive methods for smell disorder diagnostics. In recent years, increasing attention is being paid to olfactory event-related potentials (OERPs) of the brain, as a viable tool for the objective assessment of olfactory dysfunctions. The aim of this review is to describe the main features of OERPs signals, the most widely used recording and processing techniques, and the scientific progress and relevance in the use of OERPs in many important application fields. In particular, the innovative role of OERPs is exploited in olfactory disorders that can influence emotions and personality or can be potential indicators of the onset or progression of neurological disorders. For all these reasons, this review presents and analyzes the latest scientific results and future challenges in the use of OERPs signals as an attractive solution for the objective monitoring technique of olfactory disorders.

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Neural Encoding of Odors: Translating Odors into Unique Digital Representation with EEG Signals

Yadav,

Pareek,

Agarwal

et al. 2024

Lecture Notes in Computer Science

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Decoding olfactory EEG signals for different odor stimuli identification using wavelet-spatial domain feature

Cited by 14 publications

References 37 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

Assessment and Scientific Progresses in the Analysis of Olfactory Evoked Potentials

Neural Encoding of Odors: Translating Odors into Unique Digital Representation with EEG Signals

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