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Detail publikace
AMIN, H. ULLAH, R. REZA, M. MALIK, A.
Originální název
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
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
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 to smoothly operate a machine with minds.
Klíčová slova
Single trials analysis (ERPs); visual object detection; discrete wavelet transform; Huffman coding; machine learning classifiers
Autoři
AMIN, H.; ULLAH, R.; REZA, M.; MALIK, A.
Vydáno
2. 6. 2023
Nakladatel
BioMed Central
Místo
England
ISSN
1743-0003
Periodikum
Journal of NeuroEngineering and Rehabilitation
Ročník
20
Číslo
1
Stát
Švýcarská konfederace
Strany od
Strany do
17
Strany počet
URL
https://link.springer.com/article/10.1186/s12984-023-01179-8
Plný text v Digitální knihovně
http://hdl.handle.net/11012/244319
BibTex
@article{BUT184200, author="Hafeez Ullah {Amin} and Rafi {Ullah} and Mohammed Faruque {Reza} and Aamir Saeed {Malik}", title="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", journal="Journal of NeuroEngineering and Rehabilitation", year="2023", volume="20", number="1", pages="17", doi="10.1186/s12984-023-01179-8", issn="1743-0003", url="https://link.springer.com/article/10.1186/s12984-023-01179-8" }