Tinnitus EEG Classification Based on Multi-frequency Bands

Wang, Shaoju; Cai, Yixi; Sun, Zhi-Ran; Wang, Chang‐Dong; Zheng, Yangong

doi:10.1007/978-3-319-70093-9_84

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…To address the problem, some efforts are made to conduct subjectindependent experiments, which aims to distinguish tinnitus patients from control subjects. For example, Wang et al [14] adopted Fast Fourier Transform (FFT) to obtain multiple views of features from EEG signals, utilized Multi-view Intact Space Learning (MISL) to obtain latent representations, and classified samples with the Least Squares Support Vector Machine (LS-SVM).…”

Section: A Eeg-based Tinnitus Diagnosismentioning

confidence: 99%

“…When between-subject variance is high, these models may only work when the testing signals are sampled from known subjects or if their distributions closely mirror those seen within the training dataset. To enhance the model robustness, especially with regards to handling new subjects, some other research [5], [14] enables the model to be aware of the subject variance. By understanding the subject variance in signals, models can mitigate the corresponding negative influences in the prediction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

Li¹,

Liu²,

Yao³

et al. 2022

Preprint

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With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are datasetspecific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance. This paper proposes a side-aware meta-learning for crossdataset tinnitus diagnosis, which can effectively classify tinnitus in subjects of divergent ages and genders from different data collection processes. Owing to the superiority of meta-learning, our method does not rely on large-scale datasets like conventional deep learning models. Moreover, we design a subject-specific training process to assist the model in fitting the data pattern of different patients or healthy people. Our method achieves a high accuracy of 73.8% in the cross-dataset classification. We conduct an extensive analysis to show the effectiveness of side information of ears in enhancing model performance and side-aware metalearning in improving the quality of the learned features.

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Section: A Eeg-based Tinnitus Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

Li¹,

Liu²,

Yao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Then, short-time sampling from the same subjects would produce some similar samples, so subject-dependent experiments may contain similar samples in both train and test samples, which would overestimate the performance of models. Wang et al [27] studied the subject-independent experiments in classifying tinnitus patients from control subjects. It adopted FFT and concatenated the multi-view information from multiple channels and bands, which achieved good performance with the least squares SVM in a dataset of 29 volunteers.…”

Section: Related Workmentioning

confidence: 99%

Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus

Liu

Yao

Wang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEGbased tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects' EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%-94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method's stability in performance.

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“…In past studies, the time-domain features (rhythm signals) and frequency-domain features (power spectral density, PSD) of EEG signals were usually used as biomarkers to distinguish tinnitus and non-tinnitus, but these features were not obvious, and the accuracy of the identification was as high as 87% ( Wang et al, 2017 ; Vanneste et al, 2018 ). We hope that functional connectivity features can better reflect the pathological features of tinnitus, and it will greatly improve the accuracy of the identification of tinnitus by using functional connectivity features as the biomarker.…”

Section: Introductionmentioning

confidence: 99%

Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features

Wang

Shen

et al. 2022

Front. Neurosci.

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Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in electroencephalography (EEG) signals can be used as the biomarkers for an efficient and fast diagnosis method for chronic tinnitus.Methods: In this study, the resting-state EEG signals of tinnitus patients with different tinnitus locations were recorded. Four connectivity features [including the Phase-locking value (PLV), Phase lag index (PLI), Pearson correlation coefficient (PCC), and Transfer entropy (TE)] and two time-frequency domain features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources.Results: Classification accuracy was highest when the SVM algorithm or the MLP algorithm was applied to the PCC feature sets, achieving final average classification accuracies of 99.42 or 99.1%, respectively. And based on the PLV feature, the classification result was also particularly good. And MLP ran the fastest, with an average computing time of only 4.2 s, which was more suitable than other methods when a real-time diagnosis was required.Conclusion: Connectivity features of the resting-state EEG signals could characterize the differentiation of tinnitus location. The connectivity features (PCC and PLV) were more suitable as the biomarkers for the objective diagnosing of tinnitus. And the results were helpful for clinicians in the initial diagnosis of tinnitus.

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Tinnitus EEG Classification Based on Multi-frequency Bands

Cited by 12 publications

References 15 publications

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus

Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features

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