EEG-based human emotion recognition using entropy as a feature extraction measure

Patel, Pragati; Raghunandan, R; Annavarapu, Ramesh Naidu

doi:10.1186/s40708-021-00141-5

Cited by 50 publications

(17 citation statements)

References 69 publications

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“…Entropy, in general, has proven to be a practical function in extracting meaningful information from raw brain waves (see Ref. 45 for an overview and definitions of entropy). However, a significant difference is that in our experiment, the dynamic variables calculated from the collected data, including entropy, were parameterized by the ambient temperature (thermostat).…”

Section: Methodsmentioning

confidence: 99%

Collective excitations of germinating pollen grains at critical points

Pietruszka

2023

Sci Rep

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In plants, the germinating pollen grain (pollen tube) is a single, elongated cell that serves as a conduit through which gametes pass. Pollen tubes display a fast growth rate, which under certain conditions, changes periodically and is accompanied by ion exchange with the growth environment. Therefore, pollen tubes exposed to various abiotic conditions may adversely affect or improve their reproductive performance and fertility. We examined a collection of live pollen tubes of tobacco (Nicotiana tabacum L.) and hyacinth (Hyacinthus orientalis L.) using a non-invasive semiconductor–electrolyte interface technique in the vicinity of the germination temperature or optimum growth temperature of a pollen grains/tubes. The time series measurements and numerical calculations, performed using information theory methods, represent signatures of collective dynamics in living cells at critical—molecularly encoded—germination and growth temperatures. This method (and soil pH data) can facilitate assisted plant migrations from one ecosystem to another as the Earth faces climate change.

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Section: Methodsmentioning

confidence: 99%

Collective excitations of germinating pollen grains at critical points

Pietruszka

2023

Sci Rep

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“…This approach achieved a higher classification accuracy than using all channels or a random subset of channels. Patel and Patel [48] used a combination of mutual information, correlation coefficient, and principal component analysis to rank the channels, and then used a genetic algorithm to select the best subset of channels. They also achieved higher accuracy than using all channels or a random subset of channels.…”

Section: Eeg-based Signal Extraction For Emotion Recognitionmentioning

confidence: 99%

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

et al. 2023

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Emotion recognition is a vital part of human functioning. textcolorredIt enables individuals to respond suitably to environmental events and develop self-awareness. The fast-paced developments in brain–computer interfacing (BCI) technology necessitate that intelligent machines of the future be able to digitize and recognize human emotions. To achieve this, both humans and machines have relied on facial expressions, in addition to other visual cues. While facial expressions are effective in recognizing emotions, they can be artificially replicated and require constant monitoring. In recent years, the use of Electroencephalography (EEG) signals has become a popular method for emotion recognition, thanks to advances in deep learning and machine learning techniques. EEG-based systems for recognizing emotions involve measuring electrical activity in the brain of a subject who is exposed to emotional stimuli such as images, sounds, or videos. Machine learning algorithms are then used to extract features from the electrical activity data that correspond to specific emotional states. The quality of the extracted EEG signal is crucial, as it affects the overall complexity of the system and the accuracy of the machine learning algorithm. This article presents an approach to improve the accuracy of EEG-based emotion recognition systems while reducing their complexity. The approach involves optimizing the number of EEG channels, their placement on the human scalp, and the target frequency band of the measured signal to maximize the difference between high and low arousal levels. The optimization method, called the simplicial homology global optimization (SHGO), is used for this purpose. Experimental results demonstrate that a six-electrode configuration optimally placed can achieve a better level of accuracy than a 14-electrode configuration, resulting in an over 60% reduction in complexity in terms of the number of electrodes. This method demonstrates promising results in improving the efficiency and accuracy of EEG-based emotion recognition systems, which could have implications for various fields, including healthcare, psychology, and human–computer interfacing.

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“…In a review [1], it was concluded that the spectral entropy is among the best entropy measures to perform in this task. Also different entropy measures, including the spectral entropy, are used to classify emotions from EEG in a brain-computer interface, see recent review [15]. Additionally, applied to local field potential measurements, correlation of time varying spectral entropies is used to detect synchrony in neural networks [10].…”

Section: Spectral Entropymentioning

confidence: 99%

From chaos to clock in reverberating neural net. Case study

Vidybida¹,

Shchur²

2022

Preprint

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What is the reason for complex dynamical patterns registered from real biological neuronal networks? Noise and dynamical reconfiguring of a network (functional/dynamic connectome) were proposed as possible answers. In this case study, we report a complex dynamical pattern observed in a simple deterministic network of 25 neurons with fixed connectome. After a short initial stimulation, the network is engaged into a complex dynamics, which lasts for a long time. Eventually, with no external intervention, the dynamics comes to a periodic one with a short period. The long transient is positively checked for being chaotic. We conclude that the complex dynamics observed is the output of neural computation performed in the process of neuronal firings and spikes propagation.

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EEG-based human emotion recognition using entropy as a feature extraction measure

Cited by 50 publications

References 69 publications

Collective excitations of germinating pollen grains at critical points

Collective excitations of germinating pollen grains at critical points

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

From chaos to clock in reverberating neural net. Case study

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