An Analytical Study of Mental Rotation Activity Based on Feature Descriptor Patterns

• Published in: 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) pp. 1 - 5
• Main Authors: Ghosh, Sayantani, Ghosh, Lidia, Konar, Amit
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2018
• Subjects: Alzheimer's disease; Differential Evolutionary (DE) algorithm; Electroencephalography; Encoding; feature Descriptor; Feature extraction; mental rotation; power spectral density (PSD); Sociology; Task analysis
• DOI: 10.1109/WiSPNET.2018.8538540

The chief objective of this paper is to investigate the differences in Feature Descriptor patterns during mental rotation of five different objects for both healthy as well as brain diseased subjects. Thus, electroencephalographic (EEG) activity was measured during mental rotation of objects by various angles with respect to its present orientation. Source localization using eLORETA inferred an enhanced activation of pre-frontal and frontal lobe regions during mental rotation activity. Experimental analysis also confirmed maximal activation of lower alpha frequency band while performing this cognitive task. Differential Evolutionary (DE) algorithm has been implemented to select the optimal features which are represented using the Feature Descriptor diagrams. These diagrams infer that the feature patterns are distinct and vary from object to object. Moreover, these patterns orient by 45^{\mathrm {o}} for 90^{\mathrm {o}} mental rotation and by 75^{\mathrm {o}} for 180^{\mathrm {o}} mental rotation of the presented objects. However, there exists an inconsistency in the Feature descriptor diagrams for patients suffering from pre-frontal lobe amnesia and Alzheimer's disease. It is also found that these diagrams remain unaffected during mental rotation which infers their incapability to perform such a cognitive task. Hence, this work can be effectively utilized to detect people suffering from memory related disorder.