The brain is an incredibly complex organ capable of perceiving and interpreting a wide range of stimuli. Depending on individual brain chemistry and wiring, different people decipher the same stimuli differently, conditioned by their life experiences and environment. This study’s objective is to decode how the CNN models capture and learn these differences and similarities in brain waves using three publicly available EEG datasets. While being exposed to a variety of media stimuli, each brain produces unique brain waves with some similarity to other neural signals to the same stimuli. However, to figure out whether our neural models are able to interpret and distinguish the common and unique signals correctly, we employed three widely used CNN architectures to interpret brain signals. We extracted the pre-processed versions of the EEG data and identified the dependency of time windows on feature learning for song and movie classification tasks, along with analyzing the performance of models on each dataset. While the minimum length snippet of 5 s was enough for the personalized model, the maximum length snippet of 30 s proved to be the most efficient in the case of the generalized model. The usage of a deeper architecture, i.e., DeepConvNet was found to be the best for extracting personalized and generalized features with the NMED-T and SEED datasets. However, EEGNet gave a better performance on the NMED-H dataset. Maximum accuracy of 69%, 100%, and 56% was achieved in the case of the personalized model on NMED-T, NMED-H, and SEED datasets, respectively. However, the maximum accuracies dropped to 18%, 37%, and 14% on NMED-T, NMED-H, and SEED datasets, respectively, in the generalized model. We achieved a 5% improvement over the state of the art while examining shared experiences on NMED-T. This marked the outof-distribution generalization problem and signified the role of individual differences in media perception, thus emphasizing the development of personalized models along with generalized models with shared features at a certain level.

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Advances in neurotechnology have enhanced and simplified our ability to research brain activity with low-cost and effective equipment. One such scalable and noninvasive technique is Electroencephalography (EEG), which detects and records electrical brain activity. Brain activity recognition is one of the emerging problems as EEG wearables become more readily available. Our research has modeled EEG signals to classify three states (i) music listening, (ii) movie watching, and (iii) meditating. The datasets incorporating the brain signals induced while performing these activities are NMED-T for music listening, SEED for movie watching, and VIP_Y_HYT for meditating. EEG activity is transformed into deep representation using a convolutional neural network comprising three different types of 2D convolutions: Temporal, Spatial, and Separable, to capture dependencies and extract high-level features from the data. The Depthwise Convolution function is responsible for learning spatial filters within each temporal convolution, and combining these spatial filters across all temporal bands optimally is learned by the Separable Convolutions. EEGNet and EEGNet-SSVEP are specially designed for EEG Signal Processing and Classification, and the DeepConvNet has incorporated more convolution layers. Our finding demonstrates that increasing the number of layers in the Network provided a higher accuracy of 99.94% using DeepConvNet. In contrast, the accuracy of EEGNet and EEGNet-SSVEP resulted in 85.63% and 75.76%, respectively.

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Music recommendation systems struggle with predicting the aesthetic responses of listeners based solely on acoustic characteristics, which are dependent on the listener's perception. This research correlates acoustic music features with brain responses to report the neural aesthetic hypothesis that the intensity of an aesthetic experience can be decoded based on the degree of correlation to brain responses. We employ hybrid encoding-decoding model (Canonical Correlation Analysis) to identify music features that maximally covary with brain responses. EEG signals of 20 participants are analyzed while they listen to 12 songs and mark their enjoyment on a scale of 1 to 5. Firstly, 18 acoustic features are extracted from music signals and transformed into the first principal component (PC1). In addition, two other features used for analysis are root mean square (RMS) and Spectral Flux (Flux). The first principal canonical component (CC1) with PC1 determines significant (p<0.05) evidence of correlating with brain responses that increasing correlation reflects increased enjoyment. We consider each participant's average CC1 values and enjoyment rating over all 12 songs, followed by plotting a correlation graph to decode the relationship. We observe a significant (p<0.05) positive linear correlation with increasing CC1 scores of PC1 features against increased enjoyment rating. PC1 shows the maximum Pearson correlation (r = 0.48, p = 0.03). In addition, we segregate the brain responses based on low (1,2) and high (3,4) enjoyment ratings and find that higher CC1 values correspond to brain responses of high enjoyment and low values to low enjoyment in all three features. Our experiments reveal that Canonical correlation reflects music-induced pleasure and can be employed in EEG-enabled headphones to decode the user experience, leading to better recommendations.

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Entrainment is a phenomenon of phase or temporal matching of one system with that of another system. Human neural activity has been shown to resonate with external auditory stimuli. When we enjoy a piece of music, there is a resonance of brain responses with auditory signals. The crux of music cognition is based on this resonance of musical frequencies with intrinsic neural frequencies. It has also been demonstrated that the neural activities are synchronized across participants while listening to music, shown by high inter-subject correlation. In this work, we use this fact to predict the drumbeat a participant listens to based on their EEG response to the drumbeat. We also tested whether we could train on a smaller dataset and test with the rest of the dataset. We generated a frequency∗channel plot and fed it to a CNN model to predict drumbeat with a classification accuracy of 97% for 60-20-20 (train-dev-test) data split protocol and 94% accuracy for 20-20-60 data split. We also got 100% classification accuracy for predicting participants for both the data split protocols.

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Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their brain activity. In this study, we develop machine learning models to detect moments of distraction (due to mind wandering or drowsiness) during meditation practice using EEG signals. We use EEG data of 24 participants while performing a breath focus meditation with experience sampling and extract twelve linear and nonlinear EEG features. Features are fed to ten supervised machine learning models to classify (i) Breath Focus Awake (BFA) vs Breath Focus Sleepy (BFS), and (ii) BFA vs Mind Wandering (MW). We observe that the linear features achieve a maximum accuracy of 86% for classifying awake (BFA) and sleepy (BFS), whereas non-linear features have more predictive ability for classifying between BFA and MW with a maximum accuracy of nearly 78%. In addition, visualization of unsupervised t-SNE lower embeddings supports the evidence of distinct clusters for each condition. Overall our results show that machine learning algorithms can successfully identify periods of distraction during meditation practice in novice meditators based on linear and non-linear features of the EEG signal. Consequently, our results have important implications for the development of mobile EEG neurofeedback protocols aimed at facilitating meditation practice.@en

The article provides an open-source Music Listening- Genre (MUSIN-G) EEG dataset which contains 20 participants’ continuous Electroencephalography responses to 12 songs of different genres (from Indian folk music to Goth Rock to western electronic), along with their familiarity and enjoyment ratings. The participants include 16 males and 4 females, with an average age of 25.3 (+/-3.38). The EEG data was collected at the Indian Institute of Technology Gandhinagar, India, using 128 channels Hydrocel Geodesic Sensor Net (HCGSN) and the Netstation 5.4 data acquiring software. We provide the raw and partially preprocessed data of each participant while they listened to 12 different songs with closed eyes. The dataset also contains the behavioural familiarity and enjoyment ratings (scale of 1 to 5) of the participants for each of the songs. In this article, we further discuss the preprocessing steps which can be used on the dataset and prepare the data for analysis, as in the paper [1].

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Naturalistic music typically contains repetitive musical patterns that are present throughout the song. These patterns form a signature, enabling effortless song recognition. We investigate whether neural responses corresponding to these repetitive patterns also serve as a signature, enabling recognition of later song segments on learning initial segments. We examine EEG encoding of naturalistic musical patterns employing the NMED-T and MUSIN-G datasets. Experiments reveal that (a) training machine learning classifiers on the initial 20s song segment enables accurate prediction of the song from the remaining segments; (b) β and γ band power spectra achieve optimal song classification, and (c) listener-specific EEG responses are observed for the same stimulus, characterizing individual differences in music perception.

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One interpretation of breathing exercise is to enforce mind-body harmony, when someone feels well and healthy, different organs of our body function harmoniously. One dysfunctional organ may disturb the resonating mechanism across multiple organs. There are different breathing techniques, and recent scientific evidence encourages understanding the neural correlates of breathing. This research investigates breathing exercises at two paces: Rapid and Deep Slow using neural signals. We collect Electroencephalography (EEG) recordings of 14 participants performing breathing tasks. EEG signals are primarily decomposed in frequency bands that designate different cognitive functions. We extract six primary frequency bands, including delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), high beta (21-30 Hz), and gamma (30-40 Hz). Two different techniques are utilized to report the findings encompassing power spectral analysis and employing machine learning classifiers to discriminate features among different stages of inhalation and exhalation with the significance of different frequencies bands. Lowered beta power in Slow Deep breathing is observed compared to Rapid Breathing, which may suggest increased relaxation, calmness, and anxiety reduction. Differences between the two conditions observed in the frontoparietal cortex may be attributed to differences in voluntary control between the two tasks. We observed classification accuracy of 72 % using low beta between Rapid and Deep Slow breathing using Decision Tree. Several interesting findings are observed in different scalp regions suggesting future direction for further investigation. This study contributes to the understanding of neural signatures for different breathing practices. The implication of this research in health care is to design personalized therapies and to design better breathing mobile applications for daily use.

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In 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.

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Next-generation intelligent health-monitoring systems have been ushered in by mobile phones with robust computation power, affordable high-resolution cameras, and embedded wearable sensors. Wearable Electroencephalography (EEG) headset to record brain signals with simultaneous eye tracking using a mobile front camera can become a powerful tool to modulate brain activity for self-enhancement. Meditation has been proved to have great effects in neuroscientific investigations for decades. However, a naive practitioner initiates the practice with high motivation and steps down after struggling to get the feedback or follow the instructions. EEG headset and Eye-tracking technology can help beginners get real-time audio and visual feedback and encourage those who fail to continue a regular meditation session. This article proposes real-time feedback framework for generating mindful moments and trace progress while practicing. Three major components are: Learning Phase, Meditation Style Specific Feedback and Evaluation Phase. We discuss three learning moments including preparatory, disentanglement and mindful. This study facilitates the design of a neurofeedback product that can offer tailored feedback. Neurotechnological revolution enables individuals to attain better equilibrium, sustained attention, meta-cognitive awareness, decreased mind-wandering, and enhanced emotional stability through various meditation practices.@en

Modern neuroscience has shown that the brain is profoundly rhythmic and that frequencies of neural rhythms are responsive to frequencies of musical rhythms. We collected Electroencephalography (EEG) response on 12 naturalistic music stimuli (songs), from 20 participants. We retrieved the tempo and its sub-harmonics from our stimuli (songs), and further used this information to predict the beats in the brain response using Machine Learning techniques. We observed a hierarchy of beats in each of the songs, with a specific beat frequency to be dominant (i.e. higher in magnitude) than others. This led us to form three groups of songs and their brain responses, with each of the groups indicating the frequency of a beat that dominated in the hierarchy of beat structure of that song. We used small segments of 1, 3 and 5 seconds of brain responses, rather than the entire song duration. We further created two sets for classification of the three groups of brain responses and utilized two spatial filtering techniques: Mean across electrodes (ME) and first principal component (PC1), and a Dense method using data from all electrodes. This was followed by feature extraction using band power. We developed univariate and multivariate models for classification to demonstrate the significance of each frequency band which represent beat frequencies. The dense method outperformed ME and PC1. Features related to eighth note generated maximum discrimination between classes. We also observed a positive correlation between window length and rate of correct prediction. Accuracy from one second to five seconds window improved significantly in both the sets. We achieved maximum accuracy of 70% and 56% accuracies for binary and ternary classification respectively, which is 20% above chance-level accuracy. Random Forest and kNN performed better than SVM. This work contributes to the growing body of knowledge to understand the underlying neural mechanism of rhythm processing in the brain.

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The music signal comprises of different features like rhythm, timbre, melody, harmony. Its impact on the human brain has been an exciting research topic for the past several decades. Electroencephalography (EEG) signal enables the non-invasive measurement of brain activity. Leveraging the recent advancements in deep learning, we proposed a novel approach for song identification using a Convolution Neural network given the electroencephalography (EEG) responses. We recorded the EEG signals from a group of 20 participants while listening to a set of 12 song clips, each of approximately 2 minutes, that were presented in random order. The repeating nature of Music is captured by a data slicing approach considering brain signals of 1 second duration as representative of each song clip. More specifically, we predict the song corresponding to one second of EEG data when given as input rather than a complete two-minute response. We have also discussed pre-processing steps to handle large dimensions of a dataset and various CNN architectures. For all the experiments, we have considered each participant's EEG response for each song in both train and test data. We have obtained 84.96% accuracy for the same at 0.3 train-test split ratio. Moreover, our model gave commendable results as compare to chance level probability when trained on only 10% of the total dataset. The performance observed gives appropriate implication towards the notion that listening to a song creates specific patterns in the brain, and these patterns vary from person to person.

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