S., Chaudhary, ShivamChaudhary, ShivamS.P., Pandey, PankajPandey, PankajP.K.P., Miyapuram, Krishna PrasadMiyapuram, Krishna PrasadK.P.J.D., Lomas, James DerekLomas, James DerekJ.D.Mahmud, M.He, J.Vassanelli, S.van Zundert, A.Zhong, N.2025-09-012025-09-019789819698936978981969804297898196981109789819698905978303200890997898196981419783031984136978981950008697898196659389783031977718161133490302974310.1007/978-3-031-15037-1_132-s2.0-85136993889https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136993889&doi=10.1007%2F978-3-031-15037-1_13&partnerID=40&md5=c9c56271e454ad6b6ccacf6454bea76ehttps://d8.irins.org/handle/IITG2025/29410In the modern world, it is easy to get lost in thought, partly because of the vast knowledge available at our fingertips via smartphones that divide our cognitive resources and partly because of our intrinsic thoughts. In this work, we aim to find the differences in the neural signatures of mind-wandering and meditation that are common across different meditative styles. We use EEG recording done during meditation sessions by experts of different meditative styles, namely shamatha, zazen, dzogchen, and visualization. We evaluate the models using the leave-one-out validation technique to train on three meditative styles and test the fourth left-out style. With this method, we achieve an average classification accuracy of above 70%, suggesting that EEG signals of meditation techniques have a unique neural signature across meditative styles and can be differentiated from mind-wandering states. In addition, we generate lower-dimensional embeddings from higher-dimensional ones using t-SNE, PCA, and LLE algorithms and observe visual differences in embeddings between meditation and mind-wandering. We also discuss the general flow of the proposed design and contributions to the field of neuro-feedback-enabled mind-wandering detection and correction devices. � 2022 Elsevier B.V., All rights reserved.EnglishBiomedical signal processingElectroencephalographyEmbeddingsFeedbackCognitionCognitive resourcesDeep learningEEG signalsMachine-learningMeditationMind-wanderingNeural signaturesNeuro-feedbackSmart phonesConference paper202212