A Novel framework of EEG-based user identification by analyzing music-listening behavior

Kaur, Barjinder Pal; Singh, Dinesh; Roy, Partha Pratim

doi:10.1007/s11042-016-4232-2

Cited by 58 publications

(32 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the kernel was presented as "SVM," then the kernel type was not listed in the paper. Linear kernel is computationally efficient, and it was adopted in several studies [7,16,35,37,67,73,106]. Sometimes, it is impossible for the linear hyperplane to separate classes.…”

Section: Support Vector Machinementioning

confidence: 99%

A Survey on Brain Biometrics

et al. 2019

View full text Add to dashboard Cite

Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability. CCS Concepts: • Security and privacy → Biometrics; • Computing methodologies → Machine learning approaches; • Applied computing → Psychology;

show abstract

Section: Support Vector Machinementioning

confidence: 99%

A Survey on Brain Biometrics

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The recorded data were pre-processed to remove noise generated by muscular movements or external interference. The time-series signals (including EEG, GSR, and PPG) were filtered using the Savitzky-Golay (SG) filter [ 94 ], which was used for smoothing the data without distorting the signal tendency. Moreover, an on-board driven right leg (DRL) feedback circuit on Muse EEG headband canceled the noise present in the EEG signal.…”

Section: Proposed Methodologymentioning

confidence: 99%

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Raheel

Majid

Alnowami

et al. 2020

Sensors

View full text Add to dashboard Cite

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

show abstract

“…where X is input time series signal, D1 is first derivative of signal X, D2 is second derivative of signal X. -FV [20]: Spectral Entropy calculated using average band power from Eq. 10and using (18), where N b is number of bands and P ower Ratio i is defined in Eq.…”

Section: Feature Extractionmentioning

confidence: 99%

“…-FV [21]: Singular Value Decomposition (SVD) Entropy of the raw signal can be calculated using (19), where X is input time series signal, τ and d E are the delay and the embedding dimension, respectively. The embedding space is then constructed using (20).…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Text Using Emotion Detected from EEG Signals

et al. 2018

Self Cite

View full text Add to dashboard Cite

Often people might not be able to express themselves properly on social media, like not being able to think of appropriate words representative of their emotional state. In this paper, we propose an end to end system which aims to enhance user-input sentence according to his/her current emotional state. It works by a) detecting the emotion of the user and b) enhancing the input sentence by inserting emotive words to make the sentence more representative of the emotional state of the user. The emotional state of the user is recognized by analyzing the Electroencephalogram (EEG) signals from the brain. For text enhancement, we modify the words corresponding to the detected emotion using correlation finder scheme. Next, the verification of the sentence correctness has been performed using Long Short Term Memory (LSTM) Networks based Language Modeling framework. An accuracy of 74.95% has been recorded for the classification of five emotional states in a dataset of 25 participants using EEG signals. Similarly, promising results have been obtained for the task text enhancement and overall end-to-end system. To the best of our knowledge, this work is the first of its kind to enhance text according to the emotional state detected by EEG brainwaves. The system also releases an individual from thinking and typing words, which might be a complicated procedure sometimes.

show abstract

A Novel framework of EEG-based user identification by analyzing music-listening behavior

Cited by 58 publications

References 41 publications

A Survey on Brain Biometrics

A Survey on Brain Biometrics

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Enhancing Text Using Emotion Detected from EEG Signals

Contact Info

Product

Resources

About