Abstract
The instantaneous frequency measurement is the primary focus of different variably-dimensions signal processing applications. It addresses the non-stationarity of signals globally and the spread of signal frequency locally across time. The present work evaluates the effect of a mindfulness intervention on cognitive workload through different frequency step sizes utilizing one dimensional Gabor function and discrete wavelet transform (DWT) on Electroencephalogram (EEG) signals. Time–frequency tiling is carried out for four different cognitive workload levels of a visual task which shows variation in frequencies along the timeline. The statistical analysis utilizes the mean of small segments (100 ms) from long-duration (7 s) EEG data. The significant difference is found in post-meditation data than pre-meditation data for 90% subjects (18 Subjects out of 20) using a paired t-test with p-value < 0.001 for frontal and occipital electrodes from left and right hemisphere. The right hemisphere shows higher modulation of α activity than the left hemisphere. The results using the Gabor function show good performance by meeting the generalized uncertainty principle. The results with DWT show ‘what is where’ on time as well as frequency scales clearly with orthogonal basis. Moreover, the cognitive load of four levels is discriminable for an individual with pre-meditation data of twenty subjects, which is clarified with behavioral measures and using statistical analysis on objective measures (p-value < 0.05). The empirical evidence based on EEG signal analysis clarifies that the proposed method is used as an initialization step to quantify cognitive workload based on its emergent frequency at a given time scale for developmental research. This initial investigation holds relevance in determining the optimal duration for implementing meditative programs among students.
References
Slagter HA, Davidson RJ, Lutz A (2011) Mental training as a tool in the neuroscientific study of brain and cognitive plasticity. Front Hum Neurosci 5:17
Doborjeh Z, Doborjeh M, Taylor T, Kasabov N, Wang GY, Siegert R, Sumich A (2019) Spiking neural network modelling approach reveals how mindfulness training rewires the brain. Sci Rep 9(1):6367
Gao J, Sun R, Leung HK, Roberts A, Wu BWY, Tsang EW, Sik HH (2023) Increased neurocardiological interplay after mindfulness meditation: a brain oscillation-based approach. Front Hum Neurosci 17:1008490
Ng HYH, Wu CW, Huang FY, Huang CM, Hsu CF, Chao YP, ... Chuang CH (2023) Enhanced electroencephalography effective connectivity in frontal low‐gamma band correlates of emotional regulation after mindfulness training. J Neurosci Res 101(6):901–915
Naderan M, Ghoshuni M, Afrouz EP (2021) The effect of mindfulness training on creativity in healthy subjects: a pilot EEG study. Pol Psychol Bull 52(4):327–333
Shanok NA, Saldias-Manieu C, Mize KD, Chassin V, Jones NA (2023) Mindfulness-training in preadolescents in school: The role of emotionality, EEG in theta/beta bands, creativity and attention. Child Psychiatry Hum Dev 54(4):1152–1166
Li J et al (2016) Decoding EEG in cognitive tasks with time-frequency and connectivity masks. IEEE Trans Cognit Dev Syst 8(4):298–308
Do H, Hoang H, Nguyen N, An A, Chau H, Khuu Q, ... Ha H, Intermediate effects of mindfulness practice on the brain activity of college students: an EEG study. IBRO Neurosci Reports 14:308–319
Skwara AC, King BG, Zanesco AP, Saron CD (2022) Shifting baselines: longitudinal reductions in EEG beta band power characterize resting brain activity with intensive meditation. Mindfulness 13(10):2488–2506
Deng X, Yang M, Chen X, Zhan Y (2023) The role of mindfulness on theta inter-brain synchrony during cooperation feedback processing: an EEG-based hyperscanning study. Int J Clin Health Psychol 23(4):100396
Rajendran VG, Jayalalitha S, Adalarasu K (2021) EEG based evaluation of examination stress and test anxiety among college students. IRBM
Kumar TS, Kanhangad V, Pachori RB (2015) Classification of seizure and seizure-free EEG signals using local binary patterns. Biomed Signal Process Control 15:33–40
Iqbal MU, Srinivasan B, Srinivasan R (2020) Dynamic assessment of control room operator’s cognitive workload using Electroencephalography (EEG). Comput Chem Eng 141:106726
Gupta SS et al (2021) Classification of cross task cognitive workload using a deep recurrent network with modeling of temporal dynamics. Biomed Signal Proc Control 70:103070
Taori TJ, Gupta SS, Gajre SS, Manthalkar RR (2022) Cognitive workload classification: towards generalization through innovative pipeline interface using HMM. Biomed Signal Process Control 78:104010
Taori T et al (2022) Cross-task cognitive load classification with identity mapping-based distributed CNN and attention-based RNN using Gabor decomposed data images. IETE J Res, 1–17
Gupta SS, Manthalkar RR, Gajre SS (2021) Mindfulness intervention for improving cognitive abilities using EEG signal. Biomed Signal Process Control 70:103072
Eby, Lillian T et al (2019) Mindfulness-based training interventions for employees: a qualitative review of the literature. Human Resource Manag Rev 29(2):156–178
Pandey P, Miyapuram KP (2021) Nonlinear EEG analysis of mindfulness training using interpretable machine learning. In: 2021 IEEE International conference on bioinformatics and biomedicine (BIBM), pp 3051–3057. IEEE
Bing-Canar H, Pizzuto J, Compton RJ (2016) Mindfulness-of-breathing exercise modulates EEG alpha activity during cognitive performance. Psychophysiology 53(9):1366–1376
Kakumanu RJ et al (2019) State-trait influences of Vipassana meditation practice on P3 EEG dynamics. Progress Brain Res 244:115–136
Moore AW et al (2012) Regular, brief mindfulness meditation practice improves electrophysiological markers of attentional control. Front Human Neurosci 6:18
Jadhav N, Manthalkar R, Joshi Y (2016) Analysis of the effect of meditation on cognitive load using higher-order crossing features. International conference on communication and signal processing 2016 (ICCASP 2016). Atlantis Press
Egner T, Gruzelier JH (2004) EEG biofeedback of low beta band components: frequency-specific effects on variables of attention and event-related brain potentials. Clin Neurophysiol 115(1):131–139
Chow T, Javan T, Ros T, Frewen P (2017) EEG dynamics of mindfulness meditation versus alpha neurofeedback: a sham-controlled study. Mindfulness 8:572–584
Fingelkurts AA, Fingelkurts AA, KallioTamminen T (2015) EEG-guided meditation: a personalized approach. J Physiol Paris 109(4–6):180–190
Rodriguez-Larios J, Bracho Montes de Oca EA, Alaerts K (2021) The EEG spectral properties of meditation and mind wandering differ between experienced meditators and novices. NeuroImage 245:118669
Sharma H, Raj R, Juneja M (2019) EEG signal-based classification before and after combined Yoga and Sudarshan Kriya. Neurosci Lett 707:134300
Radhamani R et al (2020) Computational analysis of cortical EEG biosignals and neural dynamics underlying an integrated mind-body relaxation technique. Procedia Comput Sci 171:341–349
Kora P et al (2021) EEG based interpretation of human brain activity during yoga and meditation using machine learning: a systematic review. Complementary Therapies Clin Pract 43:101329
Rodriguez-Larios J, Wong KF, Lim J, Alaerts K (2020) Mindfulness training is associated with changes in alpha-theta cross-frequency dynamics during meditation. Mindfulness 11:2695–2704
Travis F (2020) Temporal and spatial characteristics of meditation EEG. Psychol Trauma Theory Res Pract Policy 12(2):111
Gupta SS, Manthalkar RR (2020) Classification of visual cognitive workload using analytic wavelet transform. Biomed Signal Process Control 61:101961
Wang Q, Sabrina O (2013) Real-time mental arithmetic task recognition from EEG signals. IEEE Trans Neural Syst Rehabil Eng 21(2):225–232
Fabre-Thorpe M (2011) The characteristics and limits of rapid visual categorization. Front Psychol 2:243
Keskin M et al (2019) EEG and eye-tracking user experiments for spatial memory task on maps. ISPRS Int J Geo-Inf 8(12):546
Chen C-S et al (2020) Prefrontal brain electrical activity and cognitive load analysis using a non-linear and non-stationary approach. IEEE Access 8:211115–211124
Meshram Y, Fulpatil P (2012) Review paper on electroencephalographic evaluation of Sudarshan Kriya. Int J Sci Res (IJSR) ISSN (Online), 2319–7064
Aftanas LI, Golosheikin SA (2003) Changes in cortical activity in altered states of consciousness: the study of meditation by high-resolution EEG. Hum Physiol 29:143–151
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Taori, T., Gupta, S., Manthalkar, R., Gajre, S. (2024). Mindfulness Intervention Affects Cognitive Abilities of Students: A Time–Frequency Analysis Using EEG. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds) Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics. ICEBEHI 2023. Lecture Notes in Electrical Engineering, vol 1182. Springer, Singapore. https://doi.org/10.1007/978-981-97-1463-6_15
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