scholarly journals EEG-based classification of natural sounds reveals specialized responses to speech and music

2019 ◽  
Author(s):  
Nathaniel J Zuk ◽  
Emily S Teoh ◽  
Edmund C Lalor
Keyword(s):  
Classification Accuracy ◽  
Average Power ◽  
Cost Effective ◽  
List Type ◽  
Natural Sounds ◽  
Brain Responses ◽  
Species Specific ◽  
The Brain ◽  
Better Than

AbstractHumans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical “low-level” acoustic statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the trends in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective and efficient way to study the human brain’s selectivity for speech and music in other populations.HighlightsEEG responses are stronger to speech and music than to other natural soundsThis selectivity was not replicated using stimuli with the same acoustic statisticsThese techniques can be a cost-effective way to study speech and music selectivity

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

2020 ◽  
pp. 1-2
Author(s):  
Zhang- sensen
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Brain Network ◽  
Classification Model ◽  
Comparative Experiment ◽  
Machine Learning Methods ◽  
Healthy Elderly ◽  
Healthy Elderly People ◽  
The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

scholarly journals Species-Specific Identification of Human Adenoviruses by a Multiplex PCR Assay

2000 ◽  
Vol 38 (11) ◽  
pp. 4114-4120 ◽  
Author(s):  
WanHong Xu ◽  
Mike C. McDonough ◽  
Dean D. Erdman
Keyword(s):  
Multiplex Pcr ◽  
Human Adenovirus ◽  
Cost Effective ◽  
Pcr Assay ◽  
Multiplex Pcr Assay ◽  
Amplicon Size ◽  
Species Specific ◽  
Fiber Gene ◽  
Amplification Reaction

A multiplex PCR assay was developed by using primers to the fiber gene that could differentiate human adenovirus (Ad) species A through F in a single amplification reaction. The assay correctly identified the species of all 49 recognized Ad prototype strains as well as 180 geographically and temporally diverse Ad field isolates. Ad serotype 6 (Ad6) (species C), Ad16 (species B), Ad31 (species A), and Ad40 and Ad41 (species F) could also be distinguished by amplicon size within each respective species. In comparison, a previously described Ad species-specific multiplex PCR assay that used primers to the Ad hexon gene gave equivocal results with several serotypes of species B, whereas our multiplex assay amplified all species B serotypes equally well. Our multiplex PCR assay will permit rapid, accurate, and cost-effective classification of Ad isolates.

scholarly journals Effect of Brightness of Visual Stimuli on EEG Signals

2019 ◽  
Author(s):  
Kübra Eroğlu ◽  
Temel Kayıkçıoğlu ◽  
Onur Osman
Keyword(s):  
Visual Processing ◽  
Time Window ◽  
Time Windows ◽  
Visual Stimuli ◽  
Average Power ◽  
Power Spectra ◽  
Brain Regions ◽  
Behavioral Data ◽  
Brain Responses ◽  
The Brain

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. Moreover, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used. The power spectra obtained from the analysis of the recordings using short time Fourier transform were analyzed, and a statistical analysis was performed on features extracted from these power spectra. Statistical findings obtained from electrophysiological data were compared with those obtained from behavioral data. The results showed that the brightness of visual stimuli affected the power of brain responses depending on frequency, time and location. According to the statistically verified findings, the distinctive effect of brightness occurred in the parietal and occipital regions for all the three types of stimuli. Accordingly, the increase in the brightness of pleasant and neutral images increased the average power of responses in the parietal and occipital regions whereas the increase in the brightness of unpleasant images decreased the average power of responses in these regions. However, the increase in brightness for all the three types of stimuli reduced the average power of frontal and central region responses (except for 100-300 ms time window for unpleasant stimuli). The statistical results obtained for unpleasant images were found to be in accordance with the behavioral data. The results also revealed that the brightness of visual stimuli could be represented by changing the activity power of the brain cortex. The main contribution of this research was to comprehensively examine brightness effect on brain activity for images with different emotional content and different frequency bands at different time windows of visual processing for different brain regions. The findings emphasized that the brightness of visual stimuli should be viewed as an important parameter in studies using emotional image techniques such as image classification, emotion evaluation and neuro-marketing.

scholarly journals Session to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals

2019 ◽  
Vol 15 (1) ◽  
pp. 13-27
Author(s):  
Zaineb Alhakeem ◽  
Ramzy Ali
Keyword(s):  
Independent Component Analysis ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Component Analysis ◽  
Independent Component ◽  
Learning Method ◽  
Common Spatial Pattern ◽  
Brain Signals ◽  
The Brain ◽  
Better Than

Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.

Categorical Effects in the Perception of Familiar Faces

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 71-71
Author(s):  
A Angeli ◽  
W Gerbino
Keyword(s):  
Classification Accuracy ◽  
Control Experiment ◽  
Categorical Perception ◽  
Discrimination Accuracy ◽  
Sequential Classification ◽  
Better Than ◽  
The Continuum

Photographs of morphed faces were shown to close friends of portrayed individuals. Three tasks were used: localisation of a morphed target on the continuum between the two original faces, simultaneous same - different discrimination of face pairs separated by a 20% morphing step (AB task), and sequential classification of the same pairs (ABX task). Localisation data were plotted against morph coefficients. Evidence of categorical processing was provided by steeper functions for upright vs upside-down faces. In the AB task, intermediate faces were discriminated better than faces separated by the same morphing step but closer to one original. This was confirmed in a control experiment where the participants were unfamiliar with portrayed individuals and were unlikely to process our stimuli categorically. The superiority of intermediate faces in the AB task was attributed to a nonlinearity of continua generated by the morphing procedure, and used as a baseline to evaluate ABX classification data. Also in the ABX task, intermediate faces, those straddling the categorical boundary, were classified more accurately than faces located on the same side of the boundary. However, the superiority in classification accuracy was larger than the superiority in discrimination accuracy operationalised by the AB task, as predicted by the categorical perception hypothesis.

Performance Comparison of Methods for Tree Species Classification in Multispectral Images

1970 ◽  
Vol 111 (5) ◽  
pp. 119-122 ◽  
Author(s):  
R. Dinuls ◽  
A. Lorencs ◽  
I. Mednieks
Keyword(s):  
Classification Accuracy ◽  
Field Work ◽  
Performance Comparison ◽  
Multispectral Images ◽  
Species Classification ◽  
Multispectral Data ◽  
Individual Trees ◽  
Favorable Conditions ◽  
Better Than

A number of methods for classification of individual trees in high resolution multispectral images have been developed. The paper provides comparative analysis of some practicable methods of such type. Classification accuracy into 5 species was tested by computer simulations with real multispectral data obtained using airborne hyperspectral sensor. Coordinates and species of individual trees were supplied for testing by field work. It is shown that classification accuracy better than 97 % can be reached by more sophisticated methods in favorable conditions. Presented results can be used to choose a classification method appropriate for the particular forest inventory task. Ill. 1, bibl. 7 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.371

scholarly journals Classification of EEG signal using EACA based approach at SSVEP-BCI

2021 ◽  
Vol 10 (3) ◽  
pp. 717
Author(s):  
Ashwini S. R. ◽  
H. C. Nagaraj
Keyword(s):  
Classification Accuracy ◽  
Communication Channel ◽  
Brain Activity ◽  
Computer Interfaces ◽  
Computer Device ◽  
Optimal Classification ◽  
Data Signal ◽  
The Brain ◽  
Identification Probability

The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach.

scholarly journals Energy Efficient Cluster based Routing Protocol for WSN based on Hybrid BSO-TLBO Optimization Model

2021 ◽  
Author(s):  
J Sathya Priya ◽  
Wael Mohammad Alenazy ◽  
A R Sathyabama
Keyword(s):  
Sensor Networks ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Cost Effective ◽  
Sensor Nodes ◽  
Evolving Networks ◽  
Weighted Clustering ◽  
The Brain ◽  
Better Than ◽  
The Ideal

Abstract The most famous Wireless Sensor Networks (WSN) is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method which utilizes the Brainstorm algorithm in order to adopt the ideal CH to reduce energy-draining. Further, the effectiveness of the Brain Storm Optimization (BSO) algorithm is enhanced with the incorporation of the Modified Teacher-Learner Optimized (MTLBO) algorithm with it. The modified BSO-MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inference that our approach performs better than all the other approaches.

scholarly journals Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Daliang Wang ◽  
Xiaowen Guo
Keyword(s):  
Complex System ◽  
Classification Accuracy ◽  
Music Performance ◽  
Classification Algorithm ◽  
Audio Signals ◽  
Fusion Algorithm ◽  
Before And After ◽  
Intelligent Recognition ◽  
Better Than

In the complex system of music performance, there are differences in the expression of music emotions by listeners, so it is of great significance to study the classification of different emotions under different audio signals. In this paper, the research of human emotional intelligence recognition and classification algorithm in the complex system of music performance is proposed. Through the recognition of SVM, KNN, ANN, and ID3 classifiers, the accuracy of a single classifier is compared, and then the four classifiers are combined to compare the classification accuracy of audio signals before and after preprocessing. The results show that the accuracy of SVM and ANN fusion is the highest. Finally, recall and F1 are comprehensively compared in the fusion algorithm, and the fusion classification effect of SVM and ANN is better than that of the algorithm model.

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