PSD-Based Features Extraction For EEG Signal During Typing Task

Ng, Wei Bin; Saidatul, A.; Chong, Yen Fook; Zunaidi, I.

doi:10.1088/1757-899x/557/1/012032

Cited by 27 publications

(7 citation statements)

References 9 publications

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“…The PSD was obtained by applying FFT (Fast Fourier Transform) to the power of the square to the signal. PSD was applied for each electrode [32,[54][55] to determine differences between the type of wave for each electrode.…”

Section: Methodsmentioning

confidence: 99%

Low-Cost Assessment of User eXperience Through EEG Signals

et al. 2020

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EEG signals are an important tool for monitoring the brain activity of a person, but equipment, expertise and infrastructure are required. EEG technologies are generally expensive, thus few people are normally able to use them. However, some low-cost technologies are now available. One of these is OPENBCI, but it seems that it is yet to be widely employed in Human-Computer Interaction. In this study, we used OPENBCI technology to capture EEG signals linked to brain activity in ten subjects as they interacted with two video games: Candy Crush and Geometry Dash. The experiment aimed to capture the signals while the players interacted with the video games in several situations. The results show differences due to the absence/presence of sound; players appear to be more relaxed without sound. In addition, consistent analysis of the EEG data, meCue 2.0 and SAM data showed high consistency. The evidence demonstrates that interesting results are able to be gathered based on low-cost EEG (standard) signal-based technologies.

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Section: Methodsmentioning

confidence: 99%

Low-Cost Assessment of User eXperience Through EEG Signals

et al. 2020

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“…For temporal features, we adopt Hjorth parameters, including activity, mobility and complexity, and the time-domain energy [93]. Because these temporal feature data dimension are small, we put them For frequency features, we select three types of features: differential entropy (DE) [94], power spectral density (PSD) [95] and band power. DE is defined as follows:…”

Section: Feature Extractionmentioning

confidence: 99%

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Wang

Zhang

et al. 2023

J. Neural Eng.

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Objective: Confusion is the primary epistemic emotion in the learning process, influencing students' engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what EEG signals indicate its occurrence. The present work investigates confusion during reasoning learning using EEG, and aims to fill this gap with a multidisciplinary approach combining educational psychology, neuroscience and computer science. Approach: First, we design an experiment to actively and accurately induce confusion in reasoning. Second, we propose a subjective and objective joint labeling technique to address the label noise issue. Third, to confirm that the confused state can be distinguished from the non-confused state, we compare and analyze the mean band power of confused and unconfused states across five typical bands. Finally, we present an EEG database for confusion analysis, together with benchmark results from conventional (Naive Bayes, SVM, Random Forest, and ANN) and end-to-end (LSTM, ResNet, and EEGNet) machine learning methods.Main results: Findings revealed: 1. Significant differences in the power of delta, theta, alpha, beta and lower gamma between confused and non-confused conditions; 2. A higher attentional and cognitive load when participants were confused; and 3. The Random Forest algorithm with time-domain features achieved a high accuracy/F1 score (88.06%/0.88 for the subject-dependent approach and 84.43%/0.84 for the subject-independent approach) in the binary classification of the confused and non-confused states.Significance: The study advances our understanding of confusion and provides practical insights for recognizing and analyzing it in the learning process. It extends existing theories on the differences between confused and non-confused states during learning and contributes to the cognitive-affective model. The research enables researchers, educators, and practitioners to monitor confusion, develop adaptive systems, and test recognition approaches.

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“…5) Maximum Power Spectral Density The power spectral density is the Power that is present in the signal in the frequency domain. PSD-based features have recently been used for the classification of EEG data [30], [31].…”

Section: Feature Extractionmentioning

confidence: 99%

Feature Engineering for an Efficient Motor Related EcoG BCI System

Jain

Jaiman

Baths

2023

Preprint

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Invasive Brain Computer Interface (BCI) systems through Electrocorticographic (ECoG) signals require efficient recognition of spatio-temporal patterns from a multi electrodes sensor array. Such signals are excellent candidates for automated pattern recognition through machine learning algorithms. However, the available data is limited due to the operative procedure required for such dataset creation. The importance of different temporal signatures and individual electrodes can be analyzed through feature extraction techniques. But, the variability of the signal due to non-stationarity is ignored while extracting features and which features to use can be challenging to figure out by visual inspection. In this study, we introduce the signal split parameter to account for the variability of the signal, and we use genetic selection, which allows the selection of the optimal combination of features from a pool of 8 different feature sets.

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PSD-Based Features Extraction For EEG Signal During Typing Task

Cited by 27 publications

References 9 publications

Low-Cost Assessment of User eXperience Through EEG Signals

Low-Cost Assessment of User eXperience Through EEG Signals

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Feature Engineering for an Efficient Motor Related EcoG BCI System

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