Assessing effect of meditation on cognitive workload using EEG signals

Narendra Jadhav; Ramchandra Manthalkar; Yashwant Joshi

doi:10.1117/12.2280312

19 June 2017 Assessing effect of meditation on cognitive workload using EEG signals

Narendra Jadhav, Ramchandra Manthalkar, Yashwant Joshi

Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 104431J (2017) https://doi.org/10.1117/12.2280312
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore

Abstract

Recent research suggests that meditation affects the structure and function of the brain. Cognitive load can be handled in effective way by the meditators. EEG signals are used to quantify cognitive load. The research of investigating effect of meditation on cognitive workload using EEG signals in pre and post-meditation is an open problem. The subjects for this study are young healthy 11 engineering students from our institute. The focused attention meditation practice is used for this study. EEG signals are recorded at the beginning of meditation and after four weeks of regular meditation using EMOTIV device. The subjects practiced meditation daily 20 minutes for 4 weeks. The 7 level arithmetic additions of single digit (low level) to three digits with carry (high level) are presented as cognitive load. The cognitive load indices such as arousal index, performance enhancement, neural activity, load index, engagement, and alertness are evaluated in pre and post meditation. The cognitive indices are improved in post meditation data. Power Spectral Density (PSD) feature is compared between pre and post-meditation across all subjects. The result hints that the subjects were handling cognitive load without stress (ease of cognitive functioning increased for the same load) after 4 weeks of meditation.

Citation Download Citation

Narendra Jadhav, Ramchandra Manthalkar, and Yashwant Joshi "Assessing effect of meditation on cognitive workload using EEG signals", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431J (19 June 2017); https://doi.org/10.1117/12.2280312

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