Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats

Nelly, Richard; Laursen, Bettina; Grupe, Morten; Drewes, Asbjørn Mohr; Graversen, Carina; Sørensen, Helge Bjarup Dissing; Bastlund, Jesper F.

doi:10.1088/1741-2552/aa536e

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…Recent studies also suggest that time-frequency analyses of P300 may increase its sensitivity to schizophrenia, which will also likely be useful in future rodent studies of P300 (Richard et al, 2017) geared toward treatment development.…”

Section: Conclusion: What We Now Know and Future Directionsmentioning

confidence: 99%

P300 in schizophrenia: Then and now

Hamilton,

Mathalon,

Ford

2024

Biological Psychology

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Section: Conclusion: What We Now Know and Future Directionsmentioning

confidence: 99%

P300 in schizophrenia: Then and now

Hamilton,

Mathalon,

Ford

2024

Biological Psychology

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“…In these studies, several ERP components were treated as feature sets for classification [ 29 , 30 ]. In animal studies, the ERP features such as peak amplitude and latency are also used to discriminate ERP signals [ 31 , 32 ]. However, single-trial EEG-based classification has also received much attention, since it is known that EEG data at the single-trial level possess more functional and rich information than the ERP signals obtained through the traditional grand averaging method [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Vowel speech recognition from rat electroencephalography using long short-term memory neural network

Ham

Yoo

Kim³

et al. 2022

PLoS ONE

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Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine learning methods. Nineteen male Sprague-Dawley rats subjected to microelectrode implantation surgery to record EEG signals from the bilateral anterior auditory fields were used. Five different vowel speech stimuli were chosen, /a/, /e/, /i/, /o/, and /u/, which have highly different formant frequencies. EEG recorded under randomly given vowel stimuli was minimally preprocessed and normalized by a z-score transformation to be used as input for the classification of speech recognition. The BiLSTM network showed the best performance among the classifiers by achieving an overall accuracy, f1-score, and Cohen’s κ values of 75.18%, 0.75, and 0.68, respectively, using a 10-fold cross-validation approach. These results indicate that LSTM layers can effectively model sequential data, such as EEG; hence, informative features can be derived through BiLSTM trained with end-to-end learning without any additional hand-crafted feature extraction methods.

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“…At present, the algorithms used for the detection of epilepsy mainly include artificial neural networks [3]- [5]; time-frequency analysis algorithms [6], [7]; and fuzzy clustering [8], [9], migration clustering [10]- [13], multi-view clustering [14]- [16], multitasking clustering [17] and other types of clustering algorithms [18], [19]. Liu et al [20] proposed an integrated radial basis neural network to analyze epilepsy EEG signals and improve the stability of the model.…”

Section: Introductionmentioning

confidence: 99%

Epileptic EEG Detection Using a Multi-View Fuzzy Clustering Algorithm with Multi-Medoid

Zhan

Jiang

Xia³

et al. 2019

IEEE Access

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Using clustering algorithms to automatically analyze EEGs of patients and to identify the characteristic waves of epilepsy is of high clinical value. Traditional clustering algorithms mostly use a calculated virtual single representative medoid point to describe the cluster structure, but this single representative medoid point has insufficient information. To accurately capture more accurate intracluster structural information, a representative multi-medoid points strategy is adopted, which describes the cluster structure by assigning representative weights to each sample in the cluster. Considering that the multiview learning mechanism combines information from each view to improve the algorithm's clustering performance, a multi-view fuzzy clustering algorithm with multi-medoid (MvFMMdd) is proposed. This algorithm discards the approach of the traditional fuzzy clustering algorithm, which uses a single virtual representative point to characterize the cluster structure, and uses several real representative points to describe the cluster structure. Experiments verify the medical significance of the proposed algorithm. Epileptic EEG, multi-view, multi-medoid, fuzzy clustering. INDEX TERMS

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Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats

Cited by 4 publications

References 41 publications

P300 in schizophrenia: Then and now

P300 in schizophrenia: Then and now

Vowel speech recognition from rat electroencephalography using long short-term memory neural network

Epileptic EEG Detection Using a Multi-View Fuzzy Clustering Algorithm with Multi-Medoid

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