Modulation of cortical activity in response to learning and long-term memory retrieval of 2D verses stereoscopic 3D educational contents: Evidence from an EEG study

Amin, Hafeez Ullah; Ousta, Firas; Yusoff, Mohd Zuki; Malik, Aamir Saeed

doi:10.1016/j.chb.2020.106526

Cited by 10 publications

(2 citation statements)

References 63 publications

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“…They signed a written informed consent document before starting the experiment as per the defined study protocol. This analysis is part of a research study that was approved by the Human Research Ethics Committee of the collaborating institution [28].…”

Section: Participantsmentioning

confidence: 99%

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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

Amin

Ullah

Reza

et al. 2023

J NeuroEngineering Rehabil

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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 $$4{th}$$ 4 th 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$$\pm {6.5}$$ ± 6.5 , sensitivities 93.55$$\pm {4.5}$$ ± 4.5 , specificities 94.85$$\pm {4.2}$$ ± 4.2 , precisions 92.50$$\pm {5.5}$$ ± 5.5 , and area under the curve (AUC) 0.93$$\pm {0.3}$$ ± 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 single-trial 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.

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Section: Participantsmentioning

confidence: 99%

“…This analysis is part of a research study that was approved by the Human Research Ethics Committee, Universiti Sains Malaysia [28]. All the participants signed a written informed consent document for participation.…”

Section: Author Contributionsmentioning

confidence: 99%