Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU148658" target="_blank" >RIV/00216305:26230/23:PU148658 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1186/s12984-023-01179-8" target="_blank" >https://link.springer.com/article/10.1186/s12984-023-01179-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12984-023-01179-8" target="_blank" >10.1186/s12984-023-01179-8</a>

Result language

angličtina

Original language name

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

Original language description

Background Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. Methods EEG single trials are decomposed with discrete wavelet transform (DWT) up to the 4th level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. Results The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60 +/- 6.5, sensitivities 93.55 +/- 4.5, specificities 94.85 +/- 4.2, precisions 92.50 +/- 5.5, and area under the curve (AUC) 0.93 +/- 0.3 using SVM and k-NN machine learning classifiers. Conclusion The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in singletrial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of NeuroEngineering and Rehabilitation

ISSN

1743-0003

e-ISSN

—

Volume of the periodical

20

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

1-17

UT code for WoS article

001000503100001

EID of the result in the Scopus database

2-s2.0-85160951495