A frequency domain based TEQ design for DSL systems

Lin, Yuan-Pei; Lin, Yu‐Liang; Phoong, See-May

doi:10.1109/iscas.2006.1692900

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…While listening to music, our emotions undergo transformations that can be recognized through EEG signals. Lin et al conducted a study on EEG signals collected during music listening [LWJW10]. The EEG data were obtained from 26 healthy participants, including 16 males and 10 females.…”

Section: Eeg Based Emotion Recognition In Music Listeningmentioning

confidence: 99%

“…They extracted features using power spectrum density and calculated asymmetry indexes. Additionally, they normalized the feature vectors to a range from 0 to 1 before classification and compared the performance of each feature type with the classifiers [LWJW10]. Their study revealed that the most significant changes in accuracy were attributable to a specific feature, namely spectral power asymmetry.…”

Section: Eeg Based Emotion Recognition In Music Listeningmentioning

confidence: 99%

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Emotion Recognition with Asymmetry Features of EEG Signals

Mouri

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Currently the study of affective computing (AC) includes a focus on researching emotion regulation and recognition. Recent studies in this field have utilized deep learning architectures to enhance emotion recognition from EEG signals. An alternative approach to deep learning is to use feature engineering to extract relevant features to train supervised machine learning models. Current theories in the neuroscience field can guide this feature engineering process. Neuroscientists have suggested various models to clarify how emotions are processed. One of these models suggests that positive emotions are processed in the left hemisphere, while negative emotions are processed in the right hemisphere. This emotional processing model has inspired previous studies to propose asymmetrical features to predict emotions. However, none of these studies have statistically evaluated whether the inclusion of asymmetrical features could yield benefits such as increased accuracy or reduced training time. To address that direction, this research presents both statistical evaluations for emotion regulation and a comparable model for emotion recognition. The outcomes show that brain hemispheres and frequency bands participate differently in processing emotions and observed the presence of the two asymmetry emotion processing models but in different frequency ranges. Also, the results from this study imply that by using asymmetry EEG, emotion recognition approaches can use fewer features without significantly compromising performance.

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Section: Eeg Based Emotion Recognition In Music Listeningmentioning

confidence: 99%

Section: Eeg Based Emotion Recognition In Music Listeningmentioning

confidence: 99%

Emotion Recognition with Asymmetry Features of EEG Signals

Mouri

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Filter Bank Transceivers for OFDM and DMT Systems

Lin

Phoong

Vaidyanathan

2010

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Providing key background material together with advanced topics, this self-contained book is written in an easy-to-read style and is ideal for newcomers to multicarrier systems. Early chapters provide a review of basic digital communication, starting from the equivalent discrete time channel and including a detailed review of the MMSE receiver. Later chapters then provide extensive performance analysis of OFDM and DMT systems, with discussions of many practical issues such as implementation and power spectrum considerations. Throughout, theoretical analysis is presented alongside practical design considerations, whilst the filter bank transceiver representation of OFDM and DMT systems opens up possibilities for further optimization such as minimum bit error rate, minimum transmission power, and higher spectral efficiency. With plenty of insightful real-world examples and carefully designed end-of-chapter problems this is an ideal single-semester textbook for senior undergraduate and graduate students, as well as a self-study guide for researchers and professional engineers.

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A frequency domain based TEQ design for DSL systems

Cited by 2 publications

References 7 publications

Emotion Recognition with Asymmetry Features of EEG Signals

Emotion Recognition with Asymmetry Features of EEG Signals

Filter Bank Transceivers for OFDM and DMT Systems

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