Research on multi-dimensional N-back task induced EEG variations

In order to test the effectiveness of multi-dimensional N-back task for inducing deeper brain fatigue, we conducted a series of N*L-back experiments: 1*1-back, 1*2-back, 2*1-back and 2*2-back tasks. We analyzed and compared the behavioral results, EEG variations and mutual information among these four different tasks. There was no significant difference in average EEG power and power spectrum entropy (PSE) among the tasks. However, the behavioral result of N*2-back task showed significant difference compared to traditional one dimensional N-back task. Connectivity changes were observed with the addition of one more matching task in N-back. We suggest that multi-dimensional N-back task consume more brain resources and activate different brain areas. These results provide a basis for multi-dimensional N-back tasks that can be used to induce deeper mental fatigue or exert more workload.

[1]  Hasan Ayaz,et al.  Optical brain monitoring for operator training and mental workload assessment , 2012, NeuroImage.

[2]  S. Uijtdehaage,et al.  Assessing the accuracy of topographic EEG mapping for determining local brain function. , 1998, Electroencephalography and clinical neurophysiology.

[3]  Shanguang Chen,et al.  Monitoring Mental Fatigue in Analog Space Environment Using Optical Brain Imaging , 2013 .

[4]  J. Gore,et al.  Mutual information analysis of the EEG in patients with Alzheimer's disease , 2001, Clinical Neurophysiology.

[5]  Gerald Matthews,et al.  Use of EEG Workload Indices for Diagnostic Monitoring of Vigilance Decrement , 2014, Hum. Factors.

[6]  T. Sejnowski,et al.  Estimating alertness from the EEG power spectrum , 1997, IEEE Transactions on Biomedical Engineering.

[7]  R. Krampe,et al.  Working memory and postural control: adult age differences in potential for improvement, task priority, and dual tasking. , 2009, The journals of gerontology. Series B, Psychological sciences and social sciences.

[8]  Tzyy-Ping Jung,et al.  EEG-based drowsiness estimation for safety driving using independent component analysis , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  W. Ye,et al.  Alteration of functional connectivity within visuospatial working memory-related brain network in patients with right temporal lobe epilepsy: A resting-state fMRI study , 2014, Epilepsy & Behavior.

[10]  Li Zhao,et al.  Power Spectrum Estimation of the Welch Method Based on Imagery EEG , 2013 .

[11]  S. Makeig,et al.  Lapses in alertness: coherence of fluctuations in performance and EEG spectrum. , 1993, Electroencephalography and clinical neurophysiology.

[12]  T. Inouye,et al.  Quantification of EEG irregularity by use of the entropy of the power spectrum. , 1991, Electroencephalography and clinical neurophysiology.

[13]  Tanja Schultz,et al.  Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS , 2014, Front. Hum. Neurosci..

[14]  Yoshihito Shigihara,et al.  Two different types of mental fatigue produce different styles of task performance , 2013 .

[15]  Jing Bai,et al.  Research on mental fatigue based on entropy changes in space environment , 2012, 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings.

[16]  Dong Ming,et al.  Analysis of EEG activity in response to binaural beats with different frequencies. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[17]  Yu Xiao-li.  Analysis of Mental Fatigue Basing on Power Spectrum Features of EEG , 2008 .

[18]  J. N. Kapur,et al.  Entropy optimization principles with applications , 1992 .