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

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

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.

Frontal-occipital network alterations while viewing 2D & 3D movies: a source-level EEG and graph theory approach

Many researchers have measured the differences in electroencephalography (EEG) while viewing 2D and 3D movies to uncover the neuromechanism underlying distinct viewing experiences. Using whole-brain network analyses of scalp EEG, our previous study reported that beta and gamma bands presented higher global efficiencies while viewing 3D movies. However, scalp EEG is influenced by volume conduction, not allowing inference from a neuroanatomy perspective; thus, source reconstruction techniques are recommended. This paper is the first to measure the differences in the frontal-occipital networks in EEG source space during 2D and 3D movie viewing. EEG recordings from 40 subjects were performed during 2D and 3D movie viewing. We constructed frontal-occipital networks of alpha, beta, and gamma bands in EEG source space and analyzed network efficiencies. We found that the beta band exhibited higher global efficiency in 3D movie viewing than in 2D movie viewing; however, the alpha global efficiency was not statistically significant. In addition, a support vector machine (SVM) classifier, taking functional connectivities as classification features, was built to identify whether the frontal-occipital networks contain patterns that could distinguish 2D and 3D movie viewing. Using the 6 most important functional connectivity features of the beta band, we obtained the best accuracy of 0.933. Our findings shed light on uncovering the neuromechanism underlying distinct experiences while viewing 2D and 3D movies.