Dataset Information

Time-frequency dynamics of error monitoring in childhood: An EEG study.

ABSTRACT: Error monitoring allows individuals to monitor and adapt their behavior by detecting errors. Error monitoring is thought to develop throughout childhood and adolescence. However, most of this evidence comes from studies in late childhood and adolescence utilizing event-related potentials (ERPs). The current study utilizes time-frequency (TF) and connectivity analyses to provide a comprehensive examination of age-related changes in error-monitoring processes across early childhood (N = 326; 50.9% females; 4-9 years). ERP analyses indicated the presence of the error-related negativity (ERN) and error positivity (Pe) across all ages. Results showed no error-specific age-related changes in the ERN and the Pe. However, TF analyses suggested error-related frontocentral responses in delta and theta signal strength (power), delta consistency (intertrial phase synchrony), and delta synchrony (interchannel phase synchrony) between frontrocentral and frontolateral clusters-all of which increased with age. Additionally, the current study examines the reliability and effect size estimates of the ERP and TF measures. For most measures, more trials were needed to achieve acceptable reliability than what is commonly used in the psychophysiological literature. Resources to facilitate the measurement and reporting of reliability are provided. Overall, findings highlight the utility of TF analyses and provide useful information for future studies examining the development of error monitoring.

SUBMITTER: Morales S

PROVIDER: S-EPMC9203655 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Time-frequency dynamics of error monitoring in childhood: An EEG study.

Morales Santiago S Bowers Maureen E ME Leach Stephanie C SC Buzzell George A GA Fifer William W Elliott Amy J AJ Fox Nathan A NA

Developmental psychobiology 20220401 3

Error monitoring allows individuals to monitor and adapt their behavior by detecting errors. Error monitoring is thought to develop throughout childhood and adolescence. However, most of this evidence comes from studies in late childhood and adolescence utilizing event-related potentials (ERPs). The current study utilizes time-frequency (TF) and connectivity analyses to provide a comprehensive examination of age-related changes in error-monitoring processes across early childhood (N = 326; 50.9% ...[more]

PMID: 35312050

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Project description:Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73.8 % ⁻ 86.2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

Project description:IntroductionElectroencephalogram (EEG) acquisition is easily affected by various noises, including those from electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG). Because noise interference can significantly limit the study and analysis of brain signals, there is a significant need for the development of improved methods to remove this interference for more accurate measurement of EEG signals.MethodsBased on the non-linear and non-stationary characteristics of brain signals, a strategy was developed to denoise brain signals using a time-frequency denoising algorithm framework of short-time Fourier transform (STFT), bidimensional empirical mode decomposition (BEMD), and non-local means (NLM). Time-frequency analysis can reveal the signal frequency component and its evolution process, allowing the elimination of noise according to the signal and noise distribution. BEMD can be used to decompose the time-frequency signals into sub-time-frequency signals for noise removal at different scales. NLM relies on structural self-similarity to locally smooth an image to remove noise and restore its main geometric structure, making this method appropriate for time-frequency signal denoising.ResultsThe experimental results show that the proposed method can effectively suppress the high-frequency components of brain signals, resulting in a smoother brain signal waveform after denoising. The correlation coefficient of the reference signal, a superposition average of multiple trial signals, and the original single trial signal was determined, and then correlation coefficients were calculated between the reference signal and single trial signals processed by time-frequency denoising, ensemble empirical mode decomposition (EEMD)-independent component analysis (ICA), EEMD-canonical correlation analysis (CCA), and wavelet threshold denoising methods. The correlation coefficient was highest for the signal processed by the time-frequency denoising method and the reference signal, indicating that the single trial signal after time-frequency denoising was most similar to the waveform of the reference signal and suggesting this is a feasible strategy to effectively reduce noise and more accurately determine signals.DiscussionThe proposed time-frequency denoising method exhibits excellent performance with promising potential for practical application.

Dataset Information

Time-frequency dynamics of error monitoring in childhood: An EEG study.

Publications

Time-frequency dynamics of error monitoring in childhood: An EEG study.

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