Neural signatures for the western classification of emotions have been widely discussed in the li... more Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa—as opposed to a pure emotion—is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed in the text, dedicated brain imaging studies have not been conducted in their research. Our study examines the neural oscillations, recorded through electroencephalography (EEG) imaging, that are elicited while experiencing emotional states corresponding to Rasas. We identify differences among them using network-based functional connectivity metrics in five different frequency bands. Further, Random Forest models are trained on the extracted network features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exh...
Several Convolutional Deep Learning models have been proposed to classify the cognitive states ut... more Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with millions of parameters, which increases train and test time, making the model complex and less suitable for real-time analysis. This paper proposes a simple, lightweight CNN model to classify cognitive states from Electroencephalograph (EEG) recordings. We develop a novel pipeline to learn distinct cognitive representation consisting of two stages. The first stage is to generate the 2D spectral images from neural time series signals in a particular frequency band. Images are generated to preserve the relationship between the neighboring electrodes and the spectral property of the cognitive events. The second is to develop a time-efficient, computationally less loaded, and high-performing model. We design a network containing 4 blocks and major components include standard and depth-wise convolution for increasing the performance and followed by separable convolution to decrease the number of parameters which maintains the tradeoff between time and performance. We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. We attain comparable performance utilizing less than 4% of the parameters of other models. This model can be employed in a real-time computation environment such as neurofeedback.
Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such ... more Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consumer preferences can be effectively predicted by understanding changes in evoked responses as captured by EEG. However, understanding ordered preference of choices was not studied earlier. In this study, we try to decipher the evoked responses using EEG while participants were presented with naturalistic stimuli i.e. movie trailers. Using Machine Learning tech niques to mine the patterns in EEG signals, we predicted the movie rating with more than above-chance, 72% accuracy. Our research shows that neural correlates can be an effective predictor of consumer choices and can significantly enhance our understanding of consumer behavior.
Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neur... more 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 non-linear 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 feature...
Data Description:
The Institute Ethical Committee (IEC) of Indian Institute of Technology, Gandh... more Data Description:
The Institute Ethical Committee (IEC) of Indian Institute of Technology, Gandhinagar, approved this study. Prior to conducting experiments, all of the participants provided informed consent.
Participants:
The study involved 20 healthy (mean age: 26 years, 16 males, 4 females), right-handed students from Indian Institute of Technology Gandhinagar. All participants were proficient in the Hindi language, which was also the language of the video clips. All participants were briefed about the task and asked to maintain their attention while watching the film clips. Small groups of subjects independently scored movie clips from each category of emotion. Only those clips were selected with the highest ranking for evoking a particular response for all categories.
Audio‑visual stimuli:
Bollywood is popular Indian cinema based on the Hindi language. We selected nine Bollywood movie clips covering four decades from the 1980s to recent. These movie clips depicted each Rasa and selection was based on the independent rating from a small group of participants. Each film segment had a different length because the clips contained narration that had to be shown for a certain time to evoke a specific Rasa. Film clips ranged in length from 42 s to 2 min 37 s.
EEG data acquisition:
EEG recordings were collected while a participant was asked to watch the selected nine film clips corresponding to nine Rasa s. A high-density Geodesic system of 128 channels was used for this acquisition with a sampling rate of 250 Hz. A white fixation cross on a blank screen preceded each film clip for 10 s, and the order of the films was randomized for each participant. The complete experiment was designed and run in E-prime and recordings were captured using Netstation.
Neural signatures for the western classification of emotions have been widely discussed in the li... more Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa—as opposed to a pure emotion—is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed in the text, dedicated brain imaging studies have not been conducted in their research. Our study examines the neural oscillations, recorded through electroencephalography (EEG) imaging, that are elicited while experiencing emotional states corresponding to Rasas. We identify differences among them using network-based functional connectivity metrics in five different frequency bands. Further, Random Forest models are trained on the extracted network features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exh...
Several Convolutional Deep Learning models have been proposed to classify the cognitive states ut... more Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with millions of parameters, which increases train and test time, making the model complex and less suitable for real-time analysis. This paper proposes a simple, lightweight CNN model to classify cognitive states from Electroencephalograph (EEG) recordings. We develop a novel pipeline to learn distinct cognitive representation consisting of two stages. The first stage is to generate the 2D spectral images from neural time series signals in a particular frequency band. Images are generated to preserve the relationship between the neighboring electrodes and the spectral property of the cognitive events. The second is to develop a time-efficient, computationally less loaded, and high-performing model. We design a network containing 4 blocks and major components include standard and depth-wise convolution for increasing the performance and followed by separable convolution to decrease the number of parameters which maintains the tradeoff between time and performance. We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. We attain comparable performance utilizing less than 4% of the parameters of other models. This model can be employed in a real-time computation environment such as neurofeedback.
Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such ... more Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consumer preferences can be effectively predicted by understanding changes in evoked responses as captured by EEG. However, understanding ordered preference of choices was not studied earlier. In this study, we try to decipher the evoked responses using EEG while participants were presented with naturalistic stimuli i.e. movie trailers. Using Machine Learning tech niques to mine the patterns in EEG signals, we predicted the movie rating with more than above-chance, 72% accuracy. Our research shows that neural correlates can be an effective predictor of consumer choices and can significantly enhance our understanding of consumer behavior.
Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neur... more 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 non-linear 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 feature...
Data Description:
The Institute Ethical Committee (IEC) of Indian Institute of Technology, Gandh... more Data Description:
The Institute Ethical Committee (IEC) of Indian Institute of Technology, Gandhinagar, approved this study. Prior to conducting experiments, all of the participants provided informed consent.
Participants:
The study involved 20 healthy (mean age: 26 years, 16 males, 4 females), right-handed students from Indian Institute of Technology Gandhinagar. All participants were proficient in the Hindi language, which was also the language of the video clips. All participants were briefed about the task and asked to maintain their attention while watching the film clips. Small groups of subjects independently scored movie clips from each category of emotion. Only those clips were selected with the highest ranking for evoking a particular response for all categories.
Audio‑visual stimuli:
Bollywood is popular Indian cinema based on the Hindi language. We selected nine Bollywood movie clips covering four decades from the 1980s to recent. These movie clips depicted each Rasa and selection was based on the independent rating from a small group of participants. Each film segment had a different length because the clips contained narration that had to be shown for a certain time to evoke a specific Rasa. Film clips ranged in length from 42 s to 2 min 37 s.
EEG data acquisition:
EEG recordings were collected while a participant was asked to watch the selected nine film clips corresponding to nine Rasa s. A high-density Geodesic system of 128 channels was used for this acquisition with a sampling rate of 250 Hz. A white fixation cross on a blank screen preceded each film clip for 10 s, and the order of the films was randomized for each participant. The complete experiment was designed and run in E-prime and recordings were captured using Netstation.
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Papers by Pankaj Pandey
The Institute Ethical Committee (IEC) of Indian Institute of Technology, Gandhinagar, approved this study. Prior to conducting experiments, all of the participants provided informed consent.
Participants:
The study involved 20 healthy (mean age: 26 years, 16 males, 4 females), right-handed students from Indian Institute of Technology Gandhinagar. All participants were proficient in the Hindi language, which was also the language of the video clips. All participants were briefed about the task and asked to maintain their attention while watching the film clips. Small groups of subjects independently scored movie clips from each category of emotion. Only those clips were selected with the highest ranking for evoking a particular response for all categories.
Audio‑visual stimuli:
Bollywood is popular Indian cinema based on the Hindi language. We selected nine Bollywood movie clips covering four decades from the 1980s to recent. These movie clips depicted each Rasa and selection was based on the independent rating from a small group of participants. Each film segment had a different length because the clips contained narration that had to be shown for a certain time to evoke a specific Rasa. Film clips ranged in length from 42 s to 2 min 37 s.
EEG data acquisition:
EEG recordings were collected while a participant was asked to watch the selected nine film clips corresponding to nine Rasa s. A high-density Geodesic system of 128 channels was used for this acquisition with a sampling rate of 250 Hz. A white fixation cross on a blank screen preceded each film clip for 10 s, and the order of the films was randomized for each participant. The complete experiment was designed and run in E-prime and recordings were captured using Netstation.
The Institute Ethical Committee (IEC) of Indian Institute of Technology, Gandhinagar, approved this study. Prior to conducting experiments, all of the participants provided informed consent.
Participants:
The study involved 20 healthy (mean age: 26 years, 16 males, 4 females), right-handed students from Indian Institute of Technology Gandhinagar. All participants were proficient in the Hindi language, which was also the language of the video clips. All participants were briefed about the task and asked to maintain their attention while watching the film clips. Small groups of subjects independently scored movie clips from each category of emotion. Only those clips were selected with the highest ranking for evoking a particular response for all categories.
Audio‑visual stimuli:
Bollywood is popular Indian cinema based on the Hindi language. We selected nine Bollywood movie clips covering four decades from the 1980s to recent. These movie clips depicted each Rasa and selection was based on the independent rating from a small group of participants. Each film segment had a different length because the clips contained narration that had to be shown for a certain time to evoke a specific Rasa. Film clips ranged in length from 42 s to 2 min 37 s.
EEG data acquisition:
EEG recordings were collected while a participant was asked to watch the selected nine film clips corresponding to nine Rasa s. A high-density Geodesic system of 128 channels was used for this acquisition with a sampling rate of 250 Hz. A white fixation cross on a blank screen preceded each film clip for 10 s, and the order of the films was randomized for each participant. The complete experiment was designed and run in E-prime and recordings were captured using Netstation.