EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings

Hadjidimitriou, Stelios; Hadjileontiadis, Leontios

doi:10.1109/t-affc.2013.6

Cited by 91 publications

(53 citation statements)

References 30 publications

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“…Soleymani et al [3] proposed a user-independent emotion recognition method using EEG, pupillary response and gaze distance, which achieved the best classification accuracies of 68.5% for three labels of valence and 76.4% for three labels of arousal using a modality fusion across 24 participants. Hadjidimitriou et al [43] employed three time-frequency distributions (spectrogram, Hilbert-Huang spectrum, and ZhaoAtlas-Marks transform) as features to classify ratings of liking and familiarity. They also investigated the time course of music-induced affect responses and the role of familiarity.…”

Section: Related Workmentioning

confidence: 99%

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Zheng

2015

IEEE Trans. Auton. Mental Dev.

1,396

377

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To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9 and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65%, which is even better than that of the original 62 channels. The critical frequency bands and channels determined by using the weights of trained DBNs are consistent with the existing observations. In addition, our experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals. We compare the performance of deep models with shallow models. The average accuracies of DBN, SVM, LR and KNN are 86.08%, 83.99%, 82.70% and 72.60%, respectively.

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Section: Related Workmentioning

confidence: 99%

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Zheng

2015

IEEE Trans. Auton. Mental Dev.

1,396

377

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“…In these studies, brain signals were recorded using an EEG headset while the subject listens to music [44,53,58,100,110,112,115,116,151,154,190,205,216,220,222,235,276,279]. Moreover, the subjects' emotions were recognized as displayed by EEG signals.…”

Section: Domain Description Referencesmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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“…To increase frequency resolution, we divided the β rhythm into β low (13)(14)(15)(16)(17)(18)(19)(20), and β high (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) sub-bands and derived descriptors separately. Similarly, the γ rhythm was divided into γ low (30-49Hz) and γ high (51-90Hz).…”

Section: Discussionmentioning

confidence: 99%

Musical NeuroPicks: A consumer-grade BCI for on-demand music streaming services

2018

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We investigated the possibility of using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener's subjective experience of music into scores that can be used in popular on-demand music streaming services.Our study resulted into two variants, differing in terms of performance and execution time, and hence, subserving distinct applications in online streaming music platforms. The first method, NeuroPicks, is extremely accurate but slower. It is based on the well-established neuroscientific concepts of brainwave frequency bands, activation asymmetry index and cross frequency coupling (CFC). The second method, NeuroPicks VQ , offers prompt predictions of lower credibility and relies on a custom-built version of vector quantization procedure that facilitates a novel parameterization of the music-modulated brainwaves.Beyond the feature engineering step, both methods exploit the inherent efficiency of extreme learning machines (ELMs) so as to translate, in a personalized fashion, the derived patterns into a listener's score. NeuroPicks method may find applications as an integral part of contemporary music recommendation systems, while NeuroPicks VQ can control the selection of music tracks. Encouraging experimental results, from a pragmatic use of the systems, are presented.

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EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings

Cited by 91 publications

References 30 publications

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Musical NeuroPicks: A consumer-grade BCI for on-demand music streaming services

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