EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86 % using only 24 sensors with AR 20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification.

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

An Efficient Data Transmission in Cognitive Radio Networks Using Momentum Search Algorithm

Data Transmission plays an important role in the digital world. In here, We are using Cognitive Radio(CR) a concept on Wireless Sensor Networks(WSN) which is being used as an intelligent wireless Communication Technology having unique Capabilities of monitoring spectrum bands and detecting available channels to enable the usage of statically allocated spectrum Furthermore, by dynamically adjusting its operating parameters, it can utilize available channels and to attack the upcoming spectrum crunch issue. Cognitive Radios can be used to find unused licensed spectrum and it can be utilized by secondary users without causing any interference to licensed users. Existing technologies used in cognitive radio include energy sensing, spectrum databases, and spectrum sensing using pilot channels. In small networks, transmission of small packet size can be transmitted with high efficiency without delay, whereas transmission of large data packets can cause data corruption, data packet corruption and may require retransmission over higher frequency channels. To avoid this type of interference, users need higher efficiency and wider bandwidth for efficient transmission. Here we use the technique of momentum search algorithms working on the law of conservation of momentum and the law of conservation of kinetic energy. Data transferred using this method is always unaltered. The transmitted data is split into fixed-size 64-bit packets. And the channel selection will be changed accordingly for higher channel selection efficiency for lossless data transmission. The rules of the Momentum Search algorithm allow users to transmit larger data packets with higher efficiency with the same level of interference as the primary user (PU). This proposal shows how to achieve the highest level of data transmission performance using a cognitive wireless network based on a Momentum search algorithm.

Chinese consumers' selection of wine purchasing channels: influence of demographic characteristics, perceived value factors, social factors and wine knowledge

PurposeThe selection of purchasing channels by wine consumers indirectly affects buying experience and satisfaction, therefore, it is of great practical significance to study consumers' preference on channel selection. The purpose of this study is to investigate the current state of consumer selection for purchasing channel and the corresponding influencing factors.Design/methodology/approachA total of 2,976 valid questionnaires were collected by convenience sampling from 34 provinces, municipalities and autonomous regions of China in 2020 via the Internet, yielding a response rate of 82.2%. A categorical statistical approach was used to understand consumer's selection for each channel. Besides, binary logistic regression model was used to analyze the factors affecting consumers' channel selection.FindingsThe results show that Chinese wine consumers' main purchasing channels are as follows: supermarket/mall, wine specialty stores, comprehensive e-business flagship stores, comprehensive e-business individual stores, restaurants and short video and live streaming platforms. Estimation results showed that among the 12 influencing factors in 4 categories, consumers' education and some other factors significantly influenced consumers' decision on wine purchasing channels.Research limitations/implicationsLimited by time and experimental conditions, this study did not analyze the trend of wine consumers' purchasing channel selection. Future work would concentrate on multi-year data and conduct longitudinal comparative analysis.Originality/valueThis study innovatively subdivides the currently popular wine sales channels in Chinese market and conducts research related to consumer channel selection. The results of the study can provide reference for wine producers and distributors to update their strategic layout and also help various channels to understand the characteristics of their customer groups for targeted marketing.

A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.