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

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 [Formula: see text] 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[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] 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.

Keywords: Discrete wavelet transform; Huffman coding; Machine learning classifiers; Single trials analysis (ERPs); Visual object detection.

Fig. 1

An illustration of the visual object detection task

Fig. 2

EEG preprocessing for ERPs analysis

Fig. 3

Illustration of the proposed method for single-trial analysis and classification of visual events

Fig. 4

Fourth-level DWT Decomposition, where d1 refers to detailed coefficients and a1 denotes the approximation coefficients at level 1, L refers to level, Freq. stands for frequency in hertz

Fig. 5

Bi-orthogonal B-Spline mother wavelet (bior3.5) analysis and synthesis

Fig. 6

Average ERP from 40 trials of one subject at Pz a target trials and b standard trials. Single trials bandpass EEG signal (black), reconstructed ERP signal from optimum wavelet coefficients (red) of one subject (five out of 40 trials) at Pz c target trials, and d standard trials

Fig. 7

Illustration of a Confusion Matrix and b Area under the ROC Curve of SVM classifier, c Confusion Matrix and d Area under the ROC Curve of k-NN Classifier

Fig. 8

An illustration of averaged ERPs over multiple trials of a single subject for the Standard and Target events elicited at the Pz site with corresponding whole-brain topographic maps at peak amplitude, shown as a vertical black dotted line