Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Abstract

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person's difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experimentbased EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier's accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

A Mathematical Structure Underlying Sentences and Its Connection with Short–Term Memory

“…2.6∼19.5 s for a fast reader [13], and 5.3 ∼ 30.1 s for an average reader of novels, values that are well-supported by the experiments reported in the literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

“…We have shown that the capacity (expressed in words) required to process a sentence ranges from 8.3 to 61.2 words, values that can be converted into time by assuming a reading speed. This conversion gives the range 2.6~19.5 s for a fast reader [13], and 5.3~30.1 s for an average reader of novels, values that are well-supported by the experiments reported in the literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”