Single-trial-based temporal principal component analysis on extracting event-related potentials of interest for an individual subject

Zhang, Guanghui; Li, Xueyan; Lu, Yingzhi; Tiihonen, Timo; Chang, Zheng; Cong, Fengyu

doi:10.1016/j.jneumeth.2022.109768

Cited by 6 publications

(8 citation statements)

References 70 publications

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“…One further way to mitigate issues of statistical power is by turning to contemporary single-subject ERP analyses. There are now numerous methods available for single-subject analyses of ERP data (Amin et al, 2023; Kallionpää et al, 2019; Zhang et al, 2023), but the basic objective is to treat the effect size of the difference between two conditions (e.g., viewing faces vs. houses) in an individual as the dependent measure of interest before comparing this metric across groups (e.g., ADHD vs. neurotypical). Doing so cuts down on extraneous statistical noise and can result in higher statistical power.…”

Section: Discussionmentioning

confidence: 99%

Face processing in ADHD: A review of the N170 event-related potential.

Roberts,

Trossman

2024

Canadian Journal of Experimental Psychology / Revue canadienne

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Attention-deficit hyperactivity disorder (ADHD) is associated with deficits in social functioning, including peer difficulties and poor relationship quality. Little is known, however, about the integrity of foundational sociocognitive abilities that support interpersonal interactions in ADHD. Face processing—a fundamental component of social cognition—has been a popular topic of recent investigations in this area. Researchers have attempted to delineate face processing mechanisms in ADHD to elucidate social deficits often seen in the disorder. Investigating the N170 event-related potential, a neural marker of face processing, has been a popular approach in this endeavour. Here, we present two accounts that offer competing views of how social deficits might arise in those with ADHD. Next, we systematically review and synthesise the literature on the N170 in ADHD to identify whether atypicalities in sociocognitive domains like face processing occur in this patient population. Gaps in the literature are identified and concrete solutions are offered to improve future research in this area. We end by discussing immediate implications for treatment approaches designed to address widely observed social deficits in individuals with ADHD.

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Section: Discussionmentioning

confidence: 99%

Face processing in ADHD: A review of the N170 event-related potential.

Roberts,

Trossman

2024

Canadian Journal of Experimental Psychology / Revue canadienne

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“…In the literature on visual object classification based on single-trial ERP, many studies have focused on extracting specific ERP components, combining the use of ERP components, and extracting features from the whole ERP signals, such as using the component of P2, P3, and N1/ N170. For instance, Zhang, et al [13] have proposed a temporal principal component analysis-based method for N2 and P2 components extraction in single-trial for individual subjects. It also explored the influence of the number of trials (from 10 to 42 trials), on PCA decomposition by comparing temporal correlation, and spatial correlation with conventional time-domain analysis.…”

Section: Introductionmentioning

confidence: 99%

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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“…However, this is often in conflict with the real ERP waveform or topography, as individuals sometimes show substantial variance either in temporal or spatial characteristics of activation. Therefore, to overcome this problem, some advanced methods from this class of solutions attempt to extract the temporal and spatial features of ERPs of interest from single-trial EEG or individual subjects (Cong et al, 2010;Huster et al, 2020;Rissling et al, 2014;Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…The core challenge for the methods mentioned above was that the latency and phase of individual trials varied to some extent. To extract these variables of ERPs among subjects, temporal-PCA has been used to extract ERP components of interest from single trials EEG epochs of an individual and found that the number of PCs related to a specific ERP component varied across subjects (Zhang et al, 2023). These findings, in turn, reveal that the latency and phase actually vary across subjects.…”

Section: Introductionmentioning

confidence: 99%

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis

Mahini,

Zhang,

Parviainen

et al. 2023

Preprint

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Objective: Scalp electroencephalogram (EEG) provides a substantial amount of data about information processing in the human brain. In the context of conventional event-related potential (ERP) analysis, it is typically assumed that individual trials for one subject share similar properties and stem from comparable neural sources. However, group-level ERP analysis methods (including cluster analysis) can miss important information about the relevant neural process due to a rough estimation of the brain activities of individual subjects while selecting a fixed time window for all the subjects. Method: We designed a multi-set consensus clustering method to examine cognitive processes at the individual subject level. First, consensus clustering from diverse clustering methods was applied to single-trial EEG epochs of individual subjects. Next, the second level of consensus clustering was applied across the trials of each subject. Afterward, a modified time window determination is applied to identify the ERP of interest of individual subjects. Results: The proposed method was applied to real EEG data from the active visual oddball task experiment to qualify the P3 component. Our findings disclosed that the estimated time windows for individual subjects can provide more precise ERP identification than considering a fixed time window for all subjects. Moreover, based on standardized measurementerror and established bootstrap for single-trial EEG, our assessments revealed suitable stability in the calculated scores for the identified P3 component. Significance: The new method provides a realistic and information-driven understanding of the single trials' contribution towards identifying the ERP of interest in the individual subjects.

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Single-trial-based temporal principal component analysis on extracting event-related potentials of interest for an individual subject

Cited by 6 publications

References 70 publications

Face processing in ADHD: A review of the N170 event-related potential.

Face processing in ADHD: A review of the N170 event-related potential.

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

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis

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