Analysis of EEG Signals Between Motor Imaginary Tasks and Rest Condition for Biometric Application

Biometric technology has gained immense popularity as an effective solution for enhancing cybersecurity, specifically in countering financial fraud and security threats. EEG-based authentication is unique among biometric authentication methods due to its unparalleled confidentiality and non-replicability. This study explores the feasibility of using motor imagery tasks and rest conditions for human authentication. Ten physically fit subjects, aged between 20 and 28 years, participated voluntarily in the study. The subjects perform imaginary tasks involving their left and right-hand movement. Each task lasted for two minutes, separated by a one-minute break, and EEG data were collected using the EPOC+ device, which features 14-channel electrodes. The sampling frequency was set at 128 Hz. To extract relevant frequency information, Butterworth bandpass filters were employed to extract the alpha (8-13Hz), beta (14-30Hz), and gamma (30-42Hz) frequency bands. Linear features, such as power spectral density (PSD), were obtained using the Welch Method and the Burg Method, while Spectral Entropy was used to extract non-linear features. Statistical features mean, median, standard deviation, minimum, and maximum were derived from the PSD, and spectral entropy was used as input for the classifiers. Multiple classifiers, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Decision Tree and Naive Bayes, were employed for the classification task. The Welch method combined with the Support Vector Machine classifier achieved a higher classification accuracy of 96.83% for the beta waves from channels C3, C4, O1, and O2, corresponding to the frontal and occipital lobes. Interestingly, the rest conditions exhibited a higher classification accuracy of 96.83% compared to the motor-imagery tasks, which achieved 96.04%. The utilization of motor imagery tasks and rest conditions, along with the application of advanced classification techniques, holds promise for the development of robust and reliable biometric systems in cybersecurity

Room surveillance using convolutional neural networks based computer vision system

Intelligent systems are capable of performing several tasks with high reliability and efficiency. Hence, these systems were used to perform tasks which are usually done by humans. In the event of facility breach or in times when primary security systems were compromised, a call for secondary line of security is needed. In this study, it is intended to design a convolutional neural network-based computer vision system that can possibly determine whether a person entering a vicinity is authorized or not using face, height, and built recognition with gender sensitivity. The designed system was able to obtain balanced precision and recall as well as achieving more than 0.9 F1 scores. This is a complementary technology that can work with automated locks or security systems. © 2020, World Academy of Research in Science and Engineering. All rights reserved

Design and implementation of an acoustic-based car engine fault diagnostic system in the android platform

This study aims to design and implement an acoustic-based car engine fault diagnostic system in the Android platform. A smart phone running the Adroid operating system is used in order to analyze sounds coming from the car engine. Fault diagnosis covers engine start problem detection, drive-belt analysis and tune-up detection based on valve clearance. The system is equipped with signal processing algorithms that process the sounds obtained to come up with a diagnostic result and further recommend some possible solutions. The algorithm is based on the correlation coefficient of the spectral power densities (SPD), collected using two distinct clustering techniques, of the audio signals which are fed into a fuzzy logic inference system. The system design and implementation was made successful in a smart phone running in the Android platform. Results show that the system was able to diagnose the engine not just in a single brand of car models

Design and development of a musical tone detection and identification system in brain wave signals

Electroencephalogram (EEG) is one of the electrophysiological signals that possesses a high level of randomness and complexity. EEG signals are the electrical activities of the brain which reflect the different activities going on inside a human body. These signals can be stimulated.In this study, EEG signals were stimulated by acoustic energy through musical tones. Stimulated and non-stimulated EEG signals were detected and the tone of stimulation were identified. A musical piece was composed in the key of C with which the C, F and G tones were used. A total of 27 subjects were asked to listen to this piece and while they were listening, their EEG responses were captured using a 14-channel neuro headset. The captured signals were used to train different learning algorithms (Naïve Bayes, Multilayer Perceptron, Support Vector Machine, kNN and Tree) to perform detection and identification.Fourier-based filters were used to extract the EEG frequency range and wavelet denoising was performed to smoothen the signals. The statistical characteristics of the windowed power spectrum vectors of the EEG signals were obtained and were used as features. Several feature selection algorithms (ANOVA-based, Sequential, Ranking, Reliefbased) were used to determine which statistical characteristic/s of the signal could best match the target detection / identification.Classification tasks include detection whether the EEG signals are tone stimulated or not, and identification whether the EEG signals are stimulated by the C, F or G tone. Results show an accuracy of 87% to 94% in detection and 76% to 83% in identification with F-score of 82% to 92% and 66% to 80%, respectively. These were obtained using the alpha and beta power spectrum vectors which were denoised using the reverse biorthogonal (rbio) 3.1 and rbio 3.3 wavelets. The soft thresholding method used was rigorous Stein’s unbiased risk (rigrsure). The feature selection method and classifier pairs that produce high accuracies are the ranking and relief-based feature selection methods paired with a tree classifier for detection while the ANOVA-based paired with a tree classifier, for identification.An attempt to increase the number of instances was made to further investigate the performance of the classifiers. It is noticeable that there are instance imbalances for both tasks. Since machine learning algorithms work best when the number of instances of each class are roughly equal, the SMOTE algorithm was used to balance the instances. The classification was performed in WEKA considering the five aforementioned classification algorithms. The leave-one-out cross validation (LOOCV) and the ten-fold cross validation (TFCV) methods were used as test options. For the identification task, the C segment has more intances as compared to the F and G segments. To roughly balance the instances, the SMOTE algorithm was applied once with 100% increase for both the F and G segments. The percentage of CCI obtained ranges from 45% to around 56% only for both LOOCV and TFCV. The kappa values obtained roughly range from 0.2 to 0.3. The range of data reliability is from 5% to 15% which indicates fair or minimal representation of the features to the labels considered.The five initial machine learning algorithms used might not be the best considering the data sets used in this study. To further explore other learning algorithms, this study explored the utilization of the autoWEKA package for both detection and identification tasks to possibly search for an optimized learning algorithm that could match the nature of its data sets. for both feature vector sets, the RandomForest classifier was recommended by autoWEKA to yield the highest possible CCI percentage, kappa values, highest precision and recall, and lowest optimized RMSE as compared to the other available learning algorithms found in WEKA. The percentage of CCI obtained ranges from 91% to almost 99% and the kappa values obtained range from 0.82 to roughly 0.99. The range of data reliability is from 64% to 100% which indicates strong to almost perfect representation of the features to the labels considered. Result relevancy is more than 0.9 (or 90%) as indicated by precision and truly relevant results are returned at more than 0.9 (or 90%) as indicated by the recall. High scores for both precision and recall indicates that the classifier is returning accurate results, as well as returning a majority of all positive results.This study extended the practice of digital signal processing in analyzing EEG signals in relation to musical tones. Some researches focus on the given information of a complete musical piece or song such as genre and artists. The manner on how songs were created and composed is not yet thoroughly explored. This initial study on understanding how the brain responds to musical tones can be further explored by incorporating the other elements of sounds and rudiments of a musical piece

Classification of wavelet-denoised musical tone stimulated EEG signals using artificial neural networks

Electroencephalogram (EEG) signals contains information which may be of interest for a certain purpose. However, this information may be clouded by noise. The necessity of extracting this information using filtering and feature extraction techniques is of great importance. In this study, the wavelet de-noising was implemented instead of the usual frequency filter methods. Daubechies (usually denoted by \u27db\u27) wavelets (\u27db1\u27 to \u27db10\u27) were utilized to determine if wavelet-based de-noising is effective in preparing musical tone stimulated EEG signals for feature extraction leading to classification. The selection of wavelet is based on signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and correlation coefficient (R). Twelve features were used and fed into an artificial neural network for classification. Results show that among the ten wavelets used, \u27db8\u27, \u27db9\u27 and \u27db10\u27 were found to be useful having satisfied the selection criteria. The EEG signals were divided into 5 segments: Baseline, secondary baseline, C, F and G. It was found out that each segment can be classified using different wavelets with correct classification accuracy ranging from 80% to around 92%. © 2016 IEEE

Design and implementation of a cascaded adaptive neuro-fuzzy inference system for cognitive and emotional stress level assessment based on electroencephalograms and self-reports

Stress has been considered as one of the culprit in many diseases and other physical disorders. There are several methods on how to determine stress levels which are usually conducted by an experienced clinician. However, computer aided detection systems could be more objective and consistent in delivering results as basis of diagnosis and suggestions for relieving stress. In this paper, a cascaded adaptive neuro-fuzzy inference system (ANFIS) was proposed to assess the stress level in the cognitive and emotional aspect of an individual using EEG and self-reports. The two-stage ANFIS was able to predict the level of confusion, difficulty and frustration of the respondents with the task given to them. Using these factors, the system was also able to predict the level of stress that they had. Results show close proximity to the expected levels as described by the respondents through a system evaluation using the root mean square error and a parametric statistical test for significant difference

Design and development of visible light communication-based underwater communication system for recreational scuba diving

Visible light communication (VLC) is a type of data communications which uses the visible light spectrum in the 350-800nm wavelength range. Light signals are converted into electrical pulses to indicate a specific information which in this case, diving instructions. In this study, VLC is used in an underwater communication system for recreational diving activities in order to reinforce the conventional hand signaling protocols. Wearable LED-based transmitter and phototransistor-based receiver were used. The hand-held transmitter was used to emit different light pulses corresponding to 16 commands in which 13 are standard scuba diving hand signals. The goggle receiver process and translates these pulses into an audio signal which can be heard by the diver through waterproof earphones. The VLC system developed was able to achieve an average signal reception accuracy of at least 97.0% on a series of tests conducted underwater with a maximum transmitter-to-receiver distance of 5m using white LEDs. © 2020, World Academy of Research in Science and Engineering. All rights reserved

Selection of learning algorithm for musical tone stimulated wavelet de-noised EEG signal classification

The task of classifying EEG signals pose a challenge in the selection of which learning algorithm is best to provide higher classification accuracy. In this study, five well-known learning algorithms used in data mining were utilized. The task is to classify musical tone stimulated wavelet de-noised EEG signals. Classification tasks include whether the EEG signal is tone stimulated or not, and whether the EEG signal is stimulated by either the C, F or G tone. Results show higher correct classification instances (CCI) percentages and accuracies in the first classification task using the J48 decision tree as the learning algorithm. For the second classification task, the k-nn learning algorithm outruns the other classifiers but gave low accuracy and low correct classification percentage. The possibility of increasing the performance was explored by increasing the k (number of neighbors). With the increment, its produced directly proportionate in accuracy and correct classification percentage within a certain value of k. A larger k value will reduce the accuracy and the correct classification percentages

Social media engagement of young people: A sentiment analysis using natural language processing

Mental health is an integral part of human existence and is most likely underestimated when it comes to human health. The thoughts that occupy the mind affects how a person feel and it corresponds to action which may have beneficial or harmful results. Thoughts are commonly expressed in words through written or verbal communication. In this study, the sentiments of young people are determined through the responses that they provide as they share their social media engagement experiences. With Natural Language Processing (NLP) and the implementation of Valence Aware Dictionary and s Entiment Reasoner (VADER), word clouds were produced containing significant words that indicate positive and negative sentiments. Results show common words in the positive sentiment as well as in the negative sentiment that support and represent the reality of the experience of young people as they engage in different social media activities

A data analytic approach in the thematic classification of the reasons and perspectives of ddolescents’ social media engagement

Social media is one of the leading platforms where people and organizations meet. With the technological advancements in the world wide web, smart mobile devices and Internet connectivity, an increase in social media engagement is highly observable. People of all ages, one way or the other, has a social media account and their perspective, reasons, and content-preferences vary. In this study, the experience of social media engagement from the perspective of young people is analyzed and thematically classified using a data analytic approach which focuses on natural language processing (NLP). Results show that the social media engagement experience of the respondents reflects what social media is to them and for them expressed by their reasons and perspectives, respectively. The reasons of their engagement are basically connected to the contents and features of social media platforms that suit their purpose, intention, and goals of engagement expressed by textual response and analyzed by a learning algorithm that fits multiclass models for support vector machines (SVM). The profound social media engagement of the youth leads to a wide spectrum of responses and behaviors that would affect their mental health as defined by their reasons and perspectives