Musical Healing in Eastern Tajikistan: Transforming Stress and Depression through Falak Performance

Koen, Benjamin D.

doi:10.1353/amu.2007.0006

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The practice of specialized sound, prayer, and meditation facilitates the experience of cognitive flexibility, which is a core capacity and key component to experience healing [10]. In addition to cognitive flexibility being a capacity to adapt learning and skill from one domain of experience to another, in my research into healing it also represents an in between state of potentiality from which healing can emerge.…”

Section: Cognitive Flexibility and Entrainmentmentioning

confidence: 98%

Sound-Prayer-Meditation Dynamics in Health and Healing

Koen¹

2011

IJSSH

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Section: Cognitive Flexibility and Entrainmentmentioning

confidence: 98%

Sound-Prayer-Meditation Dynamics in Health and Healing

Koen¹

2011

IJSSH

Self Cite

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“…However, the magnitude and intensity of this impact depend on the state of neural cells, previous mental conditions, and individual sensitivity. In the conducted experiments, for the induction of positive and negative emotions, a sad theme [48] was used for eliciting negative emotions, and a passionate (hysterical) theme was used for eliciting positive emotions. table2 provides details of the selected music for each theme, and figure2 illustrates the order and presentation format of the musical stimuli for the participants.…”

Section: Eeg Signal Acquisitionmentioning

confidence: 99%

Unveiling the Unexplored Realm: Novel Approach for Emotion Recognition using Auditory Stimulation and EEG Signals

khoonbani,

Ramezanian

2023

Preprint

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Emotions play a vital role in understanding human behavior and interpersonal relationships. The ability to recognize emotions through Electroencephalogram (EEG) signals offers an alternative to traditional methods, such as questionnaires, enabling the identification of emotional states in a non-intrusive manner. Automatic emotion recognition holds great potential, eliminating the need for clinical examinations or physical visits, thereby contributing significantly to the advancement of Brain-Computer Interface (BCI) technology. However, one of the key challenges lies in effectively selecting and extracting relevant features from the EEG signal to establish meaningful distinctions between different emotional states. The process of feature selection is often time-consuming and demanding. In this research, we propose a groundbreaking approach for automatically identifying three emotional states (positive, negative, and neutral) by leveraging auditory stimulation of EEG signals. Our novel method directly applies the raw EEG signal to a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) architecture, bypassing the conventional feature extraction and selection steps. This unconventional approach offers a significant departure from existing literature. Our proposed network architecture comprises ten convolutional layers, followed by three LSTM layers and two fully connected layers. Through extensive simulations and evaluations on 12 active channels, our algorithm demonstrates exceptional performance, achieving an accuracy of 97.42\% and 95.23\% for the binary classification of negative and positive emotions, as well as a Cohen's Kappa coefficient of 0.96 and 0.93 for the three-class classification (negative, neutral, and positive), respectively. These promising results highlight the efficacy of our novel methodology and its potential implications in advancing emotion recognition using EEG signals.

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