Literature DB >> 25609212

Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals.

Gianluca Borghini1,2,3, Pietro Aricò4,5, Ilenia Graziani4, Serenella Salinari5, Yu Sun6, Fumihiko Taya6, Anastatios Bezerianos6, Nitish V Thakor6,7, Fabio Babiloni8.   

Abstract

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator's skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.g. attention, information processing, and working memory. A group of 10 subjects have been asked to train daily with the NASA multi-attribute-task-battery. During such training period the neuro-physiological, behavioral and subjective data have been collected. In particular, the neuro-physiological signals have been recorded on the first (T1), on the third (T3) and on the last training day (T5), while the behavioral and subjective data have been collected every day. Finally, all these data have been compared for a complete overview of the learning process and its relations with the neuro-physiological parameters. It has been shown how the integration of brain activity, in the theta and alpha frequency bands, with the autonomic parameters of heart rate and eyeblink rate could be used as metric for the evaluation of the learning progress, as well as the final training level reached by the subjects, in terms of request of cognitive resources.

Keywords:  Cognitive learning; ECG; EEG; EOG; Perceived workload; Training

Mesh:

Year:  2015        PMID: 25609212     DOI: 10.1007/s10548-015-0425-7

Source DB:  PubMed          Journal:  Brain Topogr        ISSN: 0896-0267            Impact factor:   3.020


  11 in total

1.  Changes in Electroencephalography Activity in Response to Power Mobility Training: A Pilot Project.

Authors:  Lisa K Kenyon; John P Farris; Naomi J Aldrich; Joshua Usoro; Samhita Rhodes
Journal:  Physiother Can       Date:  2020       Impact factor: 1.037

Review 2.  Consumer Behaviour through the Eyes of Neurophysiological Measures: State-of-the-Art and Future Trends.

Authors:  Patrizia Cherubino; Ana C Martinez-Levy; Myriam Caratù; Giulia Cartocci; Gianluca Di Flumeri; Enrica Modica; Dario Rossi; Marco Mancini; Arianna Trettel
Journal:  Comput Intell Neurosci       Date:  2019-09-18

Review 3.  Brain enhancement through cognitive training: a new insight from brain connectome.

Authors:  Fumihiko Taya; Yu Sun; Fabio Babiloni; Nitish Thakor; Anastasios Bezerianos
Journal:  Front Syst Neurosci       Date:  2015-04-01

4.  Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

Authors:  Pietro Aricò; Gianluca Borghini; Gianluca Di Flumeri; Alfredo Colosimo; Stefano Bonelli; Alessia Golfetti; Simone Pozzi; Jean-Paul Imbert; Géraud Granger; Raïlane Benhacene; Fabio Babiloni
Journal:  Front Hum Neurosci       Date:  2016-10-26       Impact factor: 3.169

Review 5.  The Berlin Brain-Computer Interface: Progress Beyond Communication and Control.

Authors:  Benjamin Blankertz; Laura Acqualagna; Sven Dähne; Stefan Haufe; Matthias Schultze-Kraft; Irene Sturm; Marija Ušćumlic; Markus A Wenzel; Gabriel Curio; Klaus-Robert Müller
Journal:  Front Neurosci       Date:  2016-11-21       Impact factor: 4.677

6.  Quantification of Movement-Related EEG Correlates Associated with Motor Training: A Study on Movement-Related Cortical Potentials and Sensorimotor Rhythms.

Authors:  Mads Jochumsen; Cecilie Rovsing; Helene Rovsing; Sylvain Cremoux; Nada Signal; Kathryn Allen; Denise Taylor; Imran K Niazi
Journal:  Front Hum Neurosci       Date:  2017-12-11       Impact factor: 3.169

7.  ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task.

Authors:  Deepika Dasari; Guofa Shou; Lei Ding
Journal:  Front Neurosci       Date:  2017-05-30       Impact factor: 4.677

8.  A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User's Evaluation.

Authors:  Gianluca Borghini; Pietro Aricò; Gianluca Di Flumeri; Nicolina Sciaraffa; Alfredo Colosimo; Maria-Trinidad Herrero; Anastasios Bezerianos; Nitish V Thakor; Fabio Babiloni
Journal:  Front Neurosci       Date:  2017-06-13       Impact factor: 4.677

9.  EEG-Based Cognitive Control Behaviour Assessment: an Ecological study with Professional Air Traffic Controllers.

Authors:  Gianluca Borghini; Pietro Aricò; Gianluca Di Flumeri; Giulia Cartocci; Alfredo Colosimo; Stefano Bonelli; Alessia Golfetti; Jean Paul Imbert; Géraud Granger; Railane Benhacene; Simone Pozzi; Fabio Babiloni
Journal:  Sci Rep       Date:  2017-04-03       Impact factor: 4.379

10.  Investigating Cooperative Behavior in Ecological Settings: An EEG Hyperscanning Study.

Authors:  Jlenia Toppi; Gianluca Borghini; Manuela Petti; Eric J He; Vittorio De Giusti; Bin He; Laura Astolfi; Fabio Babiloni
Journal:  PLoS One       Date:  2016-04-28       Impact factor: 3.240

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