Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis

Hadjidimitriou, Stelios; Hadjileontiadis, Leontios

doi:10.1109/tbme.2012.2217495

Cited by 230 publications

(141 citation statements)

References 36 publications

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“…It decomposes the signal into Intrinsic Mode Functions (IMF) along with a trend, and obtains instantaneous frequency data. Hadjidimitriou et al extracted HHS-based energy as the EEG features to study the music liking of the subjects [32]. They found that time-frequency features were more resistant to noise than the STFT-based features, which only extracted frequency features.…”

Section: Author and Study Year Eeg Features Extraction Methods Dimensionmentioning

confidence: 99%

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

et al. 2017

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Featured Application: The method presented in this study can be applied in many fields, such as mental health care, entertainment consumption behavior, society safety, and so on. For example, in the mental health care field, an automatic emotion analysis system can be constructed with our method to monitor the emotional variation of the subjects. With accurate and objective emotion analysis results from EEG signals, our method can provide useful treatment effect information to the medical staff.Abstract: The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%.

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Section: Author and Study Year Eeg Features Extraction Methods Dimensionmentioning

confidence: 99%

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

et al. 2017

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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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“…EEG recordings were conducted using the Emotive EPOC 14 channel EEG wireless recording headset (Emotive Systems, Inc., San Francisco, CA) (Hadjidimitriou and Hadjileontiadis, 2012). The electrode scheme was arranged according to the international 10-20 system and included active electrodes at AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF4 positions, referenced to the common mode sense (CMS-left mastoid)/driven right leg (DRL-right mastoid) ground as shown in Figure 1 (d).…”

Section: Eeg Recordingsmentioning

confidence: 99%

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

Yuvaraj

Murugappan

Ibrahim

et al. 2014

International Journal of Psychophysiology

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In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity.This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded.Multi-modal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both the groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

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Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis

Cited by 230 publications

References 36 publications

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

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

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

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