Event-related Potentials and Their Applications

Landa, Leoš; Krpoun, Zdeněk; Kolářová, Marie; Kašpárek, Tomáš

doi:10.1007/bf03379603

Cited by 14 publications

(13 citation statements)

References 55 publications

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“…Event-Related Potential (ERP) is derived from the electroencephalogram (EEG) measurement of brain response. An ERP is used to measure the voltage change results of attentive and inattentive behaviour during the experiment [38]. Figure 16 shows a plot of grand-average ERP waveform.…”

Section: B Grand-averaged Event-related Potentialmentioning

confidence: 99%

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

2021

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Attentive learning is an important feature of the learning process. It provides a beneficial learning experience and plays a key role in generating positive learning outcomes. Most studies widely applied electroencephalogram (EEG) to measure human attention level. Although most studies use EEG handcrafted features and statistical methods to classify attention level, a more effective feature learning technique is still needed. In this paper, we aim to analyze participants' EEG signals through a deep learning model and classify those signals as showing either attentive or inattentive behaviors. To carry out this research, we initially conducted a background study on attention and its detection in EEG. After that, we design a Troxler's fading experiment and use an EEG device to collect data on participants' attentive and inattentive behaviors during the test. The collected EEG data will be analyzed using a Convolution Attention Memory Neural Network (CAMNN) model to classify participants' attention level. The proposed CAMNN model is optimized with Vector-to-Vector (Vec2Vec) modeling, where the model can be learned through deep neural networks in an end-to-end approach. The result shows that our model can achieve 92% accuracy and 0.92 F1 score which outperforms several existing neural network models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), Deep Learning with Convolutional Neural Networks (deep ConvNets), and Compact Convolutional Network for EEG-based BCIs (EEGNet). This research can be useful for those who are interested in developing attention level monitoring or biofeedback system in areas such as educational classroom learning, medical research, and industrial operator.

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Section: B Grand-averaged Event-related Potentialmentioning

confidence: 99%

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

2021

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“…Panel B in Figure 8 shows the P gain calculated with equation (6) and depicts the gain (or loss) of the electrode sets selected with our methodology (described in II-F2) versus that of the standard electrode configurations currently used. As we mentioned above, positive values indicate that the accuracy of the BLDA classifier achieved with the maxAUC method was better than that of the standard electrode configurations.…”

Section: A Maxauc Erp Detection Contributes To Classification Accuracymentioning

confidence: 99%

“…For example, changes in ERPs are used as measures for diagnosing neurological and psychiatric diseases. ERPs are also employed in the study of emotions, attention, cognitive processes and personality traits [6]. However, retrieving information from ERPs remains challenging due to the small differences between an ERP and neural background activity.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Changoluisa

Varona²,

Rodrı́guez³

2020

IEEE Access

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Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter-and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.

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“…One way to distinguish between different kinds of ERP components is by the timing of their occurrence, which splits them into two classes (Landa, Krpoun, Kolarova, & Kasparek, 2014). Early wave ERPs normally peak within the first 200 milliseconds following stimulus onset and represent "sensory" or "exogenous" filtering (sensitive to the physical nature of the stimulus).…”

Section: Erp Componentsmentioning

confidence: 99%

“…Within early and late ERPs, waveforms can be further differentiated based on polarity (positive [P] or negative [N]), scalp distribution, and response to task manipulation (Landa et al, 2014). A standard, operational definition conceptualized by Donchin, Ritter, and McCallum (1978), and revised by S. Luck (2014) is as follows:…”

Section: Erp Componentsmentioning

confidence: 99%

Creative Control: A Neurocognitive Index of Relationships Between Creativity and Attention

Ahsan¹

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The relationship between attention and creativity in task-dependent situations is unclear. Previous work has shown that defocused attention is associated with divergent thinking (i.e. Carson, Peterson, & Higgins, 2003) and that focused attention is associated with convergent thinking (i.e. Necka, 1999). To address this discrepancy, we assess relationships between early forms of attention (sensory gating) and standardized creative thinking tasks. Attention is indexed by the P50 ERP component.Creative performance is tested in divergent and convergent thinking domains using the ATTA and CRA, respectively, as well as a convergent thinking, non-creative task.We present a correlational analysis between attentional style and creative performance from 22 participants. Our results show that defocused attention enhances divergent thinking, and that focused attention enhances convergent thinking, in both creative and non-creative domains. Furthermore, we demonstrate the utility of the P50 in creativity studies and suggest a methodological contribution that will extend current approaches to extracting P50 values.

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Event-related Potentials and Their Applications

Cited by 14 publications

References 55 publications

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Creative Control: A Neurocognitive Index of Relationships Between Creativity and Attention

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