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

Hafeez Ullah Amin, Rafi Ullah, Mohammed Faruque Reza, Aamir Saeed Malik

Research output: Contribution to journalArticle (journal)peer-review

2 Citations (Scopus)
12 Downloads (Pure)

Abstract

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
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
, sensitivities 93.55
, specificities 94.85
, precisions 92.50
, and area under the curve (AUC) 0.93
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.
Original languageEnglish
Article number70
Pages (from-to)1-17
Number of pages17
JournalJournal of NeuroEngineering and Rehabilitation
Volume20
Early online date19 Apr 2023
DOIs
Publication statusPublished - 2 Jun 2023

Keywords

  • ERPs
  • Huffman coding
  • machine learning
  • Machine learning classifiers

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