Influences of cognitive load on sensorimotor contributions to working memory: An EEG investigation of mu rhythm activity during speech discrimination

Jenson, David; Thornton, David; Harkrider, Ashley W.; Saltuklaroglu, Tim

doi:10.1016/j.nlm.2019.107098

Cited by 14 publications

(21 citation statements)

References 110 publications

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“…Alongside attention, working memory function is critical to the successful completion of cognitive tasks as perceived stimuli are retained for task-related processing. Neural correlates of working memory can be clearly observed in event-related EEG data (Schneider et al, 2017;Jenson et al, 2019). Many lines of research have demonstrated strong post-stimulus strong alpha and beta desynchronization following stimulus offset, which is interpreted as the processing of stimuli while held in working memory.…”

Section: Figure 1 | (A)mentioning

confidence: 99%

The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering

Jenson

Bowers

Hudock

et al. 2020

Front. Hum. Neurosci.

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Section: Figure 1 | (A)mentioning

confidence: 99%

The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering

Jenson

Bowers

Hudock

et al. 2020

Front. Hum. Neurosci.

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“…Motor imagery electroencephalography (MI-EEG) is a self-regulated EEG without an external stimulus, which can be detected by electrodes. It was suggested in a literature survey that MI is consistent with changes caused by actual exercise in the motor cortex region (Jenson et al, 2019;Kwon et al, 2019).…”

Section: Introductionmentioning

confidence: 96%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

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“…Theta power has been suggested as an indicator for the average cognitive load of subjects and the linguistic complexity of educational videos (Castro-Meneses et al, 2019). Mu rhythm oscillations (8 − 13 Hz over the sensorimotor cortex) could be affected by the cognitive load during speech perception due to attention and working memory processes (Jenson et al, 2019). In addition to the task difficulty, the listener's skill also may affect the cognitive load.…”

Section: Eeg-pc Scores Related To Task Difficulty-based Cognitive Loadmentioning

confidence: 99%

EEG Correlates of Learning From Speech Presented in Environmental Noise

et al. 2020

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Influences of cognitive load on sensorimotor contributions to working memory: An EEG investigation of mu rhythm activity during speech discrimination

Cited by 14 publications

References 110 publications

The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering

The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

EEG Correlates of Learning From Speech Presented in Environmental Noise

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