Assessing Footwear Comfort by Electroencephalography Analysis

Music genre classification (MGC) is the process of putting genre labels on music by analyzing the sounds or words. With the rapid growth of music data repositories, MGC can be used in a lot of ways to organize and manage music recommendation systems, advertising, and streaming services. But there have been a lot of works on classifying English music using different statistical and machine learning methods, but there hasn't been much progress in classifying Bengali music. Also, Deep Learning (DL) methods have been used in a few important ways to classify different types of music. The content and uniqueness of Bengali music make it much more interesting. Also, there is still a lot to learn about how to use the DL approach in Bengali music. So, Bengali music genre classification is a pretty new area of research in the field of Deep Learning. In this paper, we developed a unique technique called BMNet-5 to perform a multiclass classification of Bangla music genres such as "Bangla Adhunik," "Bangla Hip-Hop," "Bangla Band Music," "Nazrulgeeti," "Palligeeti," and "Rabindra Sangeet." We show the effectiveness of the suggested technique by extracting features from a dataset of 1742 Bangla music pieces and evaluating the automated classification judgments. The proposed BMNet-5 is based on a neural network designed to predict music genre from audio inputs. Our suggested model outperformed the corresponding previous research with an accuracy of 90.32%. The BMNet-5 model is then tested for performance consistency using K-fold cross validation with varying k values. Finally, we use the suggested model to train the interpretable SHAP model for all the genre of the Bangla music dataset, and the development of an explainable outcome may have a significant advantage.

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“…In music, tempo means how fast or slow a piece of music is played [38]. For example, a tempo of 60 BPM means that there is one beat every second.…”

Section: ) Tempomentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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“…The subject that obtained the best results in the pseudo-online analysis was S4, with the combination of H J A (8-14)/H J C (23)(24)(25)(26)(27)(28)(29)(30) features, the central electrodes configuration and a ratio of 8.8. This was the reason why it was selected for the pilot test E2.…”

Section: Exoskeleton Closed-loopmentioning

confidence: 99%

“…For the detection of motor intention, the most common paradigms are: Movement-Related Cortical Potentials (MRCPs) and Event-Related (De)Synchronization (ERD/ERS), each with different latency and frequency characteristics regarding the eliciting event. MRCP are associated with low frequency changes (0.05-3 Hz) occurring as early as 0.5-2 s. ERD/ERS is described as a change of power in the alpha (8-12 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) bands. This event is temporally characterized up to 2 s before the self-paced movement [4].…”

Section: Introductionmentioning

confidence: 99%

“…where b is the scale parameter of the distribution and a is the shape parameter and X is a continuous random variable. The Weibull distribution has been used as an indicator of a physiological neural process [22,23].…”

mentioning

confidence: 99%

“…Each of the features were computed in the different bands extracted with the state variables band-pass filters: whole spectrum (8-40 Hz), alpha (8-14 Hz), low beta (15-22 Hz), high beta (23)(24)(25)(26)(27)(28)(29)(30), low gamma (31-40 Hz) and high gamma (41-60 Hz).…”

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confidence: 99%

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Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation

et al. 2022

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Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented.

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A review on the use of eeg for the investigation of the factors that affect Consumer’s behavior

Panteli,

Kalaitzi,

Fidas

2024

Physiology & Behavior

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Assessing Footwear Comfort by Electroencephalography Analysis

Cited by 5 publications

References 27 publications

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation

A review on the use of eeg for the investigation of the factors that affect Consumer’s behavior

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