Application Of Electroencephalogram Research Articles

Application of electroencephalogram (EEG) in the study of the influence of different contents of alcohol and Baijiu on brain perception

Alcoholic beverages flavour is complex and unique with different alcohol content, and the application of flavour perception could improve the objectivity of flavour evaluation. This study utilized electroencephalogram (EEG) to assess brain reactions to alcohol percentages (5 %–53 %) and Baijiu's complex flavours. The findings demonstrate the brain's proficiency in discerning between alcohol concentrations, evidenced by increasing physiological signal strength in tandem with alcohol content. When contrasted with alcohol solutions of equivalent concentrations, Baijiu prompts a more significant activation of brain signals, underscoring EEG's capability to detect subtleties due to flavour complexity. Additionally, the study reveals notable correlations, with δ and α wave intensities escalating in response to alcohol stimulation, coupled with substantial activation in the frontal, parietal, and right temporal regions. These insights verify the efficacy of EEG in charting the brain's engagement with alcoholic flavours, setting the stage for more detailed exploration into the neural encoding of these sensory experiences.

Exploring BMI recognition through resting state, free imagination, and visual stimulation-induced EEG

In recent years, the development of Brain-Computer Interface (BCI) technology has attracted significant attention, particularly in the acquisition and application of electroencephalogram (EEG) signals. Most pertinent studies have utilized EEG to monitor neural activity states. Given that EEG does not provide direct insights into the physical states of body structures, many researchers have refrained from employing EEG to assess structural body states such as Body Mass Index (BMI). This study, for the first time, employs portable EEG detection devices combined with machine learning algorithms such as Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) to process EEG features, enabling the application of EEG in identifying BMI. We collected EEG data from three different states: resting state, free imagination, and viewing pictures. The results demonstrate that these EEG signals can effectively differentiate subjects with higher BMI. Particularly under visual stimulation, EEG features exhibited optimal performance when subjects viewed images of high-calorie foods, with the model achieving an average accuracy of 80% and an F1 score of 75%. From the perspective of frequency bands, within a single frequency band, the β band performed notably well in classification. However, the model constructed by integrating features from various frequency bands yielded the best classification performance. These comprehensive paradigms are expected to fill the current research gap in the application of EEG and provide new methodological perspectives for further studies.

Emotion Recognition from EEG Signals Using Recurrent Neural Networks

The application of electroencephalogram (EEG)-based emotion recognition (ER) to the brain–computer interface (BCI) has become increasingly popular over the past decade. Emotion recognition systems involve pre-processing and feature extraction, followed by classification. Deep learning has recently been used to classify emotions in BCI systems, and the results have been improved when compared to classic classification approaches. The main objective of this study is to classify the emotions from electroencephalogram signals using variant recurrent neural network architectures. Three architectures are used in this work for the recognition of emotions using EEG signals: RNN (recurrent neural network), LSTM (long short-term memory network), and GRU (gated recurrent unit). The efficiency of these networks, in terms of performance measures was confirmed by experimental data. The experiment was conducted by using the EEG Brain Wave Dataset: Feeling Emotions, and achieved an average accuracy of 95% for RNN, 97% for LSTM, and 96% for GRU for emotion detection problems.

The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects.

The driver is one of the most important factors in the safety of the transportation system. The driver’s perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver’s brain activity pattern, effectively acquiring driver perceptual characteristics, creating a direct connection between the driver’s brain and external devices, and realizing information interchange. This paper first introduces the theories related to EEG, then reviews the applications of EEG in scenarios such as fatigue driving, distracted driving, and emotional driving. The limitations of existing research have been identified and the prospect of EEG application in future brain-computer interface automotive assisted driving systems have been proposed. This review provides guidance for researchers to use EEG to improve driving safety. It also offers valuable suggestions for future research.

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

PurposeSteady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approachTo survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.FindingsFor the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/valueBased on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

Recognition of depression patients with electroencephalogram

The recognition of patients with depression is a very important problem, and there are few relevant studies at present. As the application of electroencephalogram (EEG) signals becomes mature in clinical diagnosis, the relationship between EEG and depression has been widely concerned. In this study, firstly, EEG signals were analysed, then EEG signals were collected for processing and feature extraction, and depression patients were recognised by the support vector machine (SVM) method. The experimental results demonstrated that SVM showed different accuracy in different features, leads, and wavebands. When all leads were used, the accuracy of SVM was the highest. When power spectral density (PSD) was used as the feature, the accuracy of SVM was higher than 70%, and its accuracy on the β wave was the highest; when activity was used as the feature, the accuracy of SVM was higher than 75%, and its accuracy was the highest on the α wave. The comparison of random forest (RF) and k-nearest neighbour (KNN) demonstrated that SVM showed the highest accuracy. The results show that the EEG signal-based method has a good performance in recognising depression patients and can be popularised and applied in practice.

Predicting Human Intention-Behavior Through EEG Signal Analysis Using Multi-Scale CNN.

At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional neural networks (CNN) has contributed to substantial improvements in the EEG signal classification performance. However, there is still a key challenge with the existing CNN-based EEG signal classification methods, the accuracy of them is not very satisfying. This is because most of the existing methods only utilize the feature maps in the last layer of CNN for EEG signal classification, which might miss some local and detailed information for accurate classification. To address this challenge, this paper proposes a multi-scale CNN model-based EEG signal classification method. In this method, first, the EEG signals are preprocessed and converted to time-frequency images using the short-time Fourier Transform (STFT) technique. Then, a multi-scale CNN model is designed for EEG signal classification, which takes the converted time-frequency image as the input. Especially, in the designed multi-scale CNN model, both the local and global information is taken into consideration. The performance of the proposed method is verified on the benchmark data set 2b used in the BCI contest IV. The experimental results show that the average accuracy of the proposed method is 73.9 percent, which improves the classification accuracy of 10.4, 5.5, 16.2 percent compared with the traditional methods including artificial neural network, support vector machine, and stacked auto-encoder.

Method for enhancing single-trial P300 detection by introducing the complexity degree of image information in rapid serial visual presentation tasks.

The application of electroencephalogram (EEG) generated by human viewing images is a new thrust in image retrieval technology. A P300 component in the EEG is induced when the subjects see their point of interest in a target image under the rapid serial visual presentation (RSVP) experimental paradigm. We detected the single-trial P300 component to determine whether a subject was interested in an image. In practice, the latency and amplitude of the P300 component may vary in relation to different experimental parameters, such as target probability and stimulus semantics. Thus, we proposed a novel method, Target Recognition using Image Complexity Priori (TRICP) algorithm, in which the image information is introduced in the calculation of the interest score in the RSVP paradigm. The method combines information from the image and EEG to enhance the accuracy of single-trial P300 detection on the basis of traditional single-trial P300 detection algorithm. We defined an image complexity parameter based on the features of the different layers of a convolution neural network (CNN). We used the TRICP algorithm to compute for the complexity of an image to quantify the effect of different complexity images on the P300 components and training specialty classifier according to the image complexity. We compared TRICP with the HDCA algorithm. Results show that TRICP is significantly higher than the HDCA algorithm (Wilcoxon Sign Rank Test, p<0.05). Thus, the proposed method can be used in other and visual task-related single-trial event-related potential detection.

A Delay and Energy Efficient Poll-Based MAC Protocol for Wireless Body Area Networks

A number of tiny sensor nodes are strategically placed in and around the human body to obtain physiological information. The sensor nodes are connected to a coordinator or a data collector to form a wireless body area network (WBAN). WBAN consists of variety of medical and non-medical applications with aggregate data rate requirement ranging from few bytes per second to 10 Mbps. These applications are having relatively different energy saving, reliability and quality of service (QoS) requirements. For example, emergency medical data are highly erratic but should be transferred with high reliability and minimum delay, whereas electrocardiogram and electroencephalogram applications are constant bit rate traffic which need to be transferred with moderate reliability. Additionally, non-medical applications include variable bit rate traffic and their jitter and delay requirements must also be met. On the above, a sensor node should spend minimum energy and conserve power to increase its life time in the network. The existing media access control (MAC) protocols present in various short and medium range wireless technologies such as 802.11 and 802.15.4 have been designed for specific purposes, and therefore, do not fulfill the diverse performance requirements across all WBAN applications. In this paper, we propose a poll-based MAC protocol, PMAC for WBAN, which can meet such diversified functional requirements of various WBAN applications. In particular, we introduce few concepts in polling based channel access mechanism to make an energy efficient and QoS aware MAC protocol. The design has been validated by obtaining the performance of proposed PMAC protocol through simulation.

Influence of light on circadian rhythm and alertness

In addition to the role in vision, light also strongly affect human health and well-being. The recent discovery of photoreceptive retinal ganglion cells (pRGCs) provides information on how the non-visual brain centers exert functional control over the circadian and neuroendocrine responses. It is found that light exposure could influence the signals timing and amplitude. Aging limits the photoreception of pRGCs due to age-related influences, which may aggravate variety of common age-associated problems. Extensive research has not only been focused on the effects of light on circadian rhythms but also the subsequent interaction with alertness. In this review, (1) the visual and non-visual pathways were compared and the suprachiasmatic nuclei (SCN)s control over physiological rhythms are summarized; (2) the mechanisms on how circadian rhythms, cognition and alertness influenced by the amplitude, timing and duration of light are analyzed; (3) the effects of ocular aging and cataract surgery on circadian photoreception are summarized; (4) the application of electroencephalogram as an effective way to evaluate alertness are also reviewed.