Neural Network Classification of Brainwave Alpha Signals in Cognitive Activities

The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different features, EEG signals are extracted using first-order extraction and are classified using the Neural Network method. The results of this study are typical of the five first-order features used, namely average, standard deviation, skewness, kurtosis, and entropy. The results of pattern recognition training show that 171 successful iterations are carried out with a period of execution of 6 seconds. Performance tests are performed using the Mean Squared Error (MSE) function. The results of the performance tests that were successfully obtained in the pattern test are in the number 0.000994

Principal component analysis implementation for brainwave signal reduction based on cognitive activity

Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form.  To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect.  Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis

Brainwave Classification for Brain Balancing Index (BBI) via 3D EEG Model Using k-NN Technique

In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application

Socio-political violence in Cambodia between 1990 and 2008: an explanatory mixed methods study of social coherence

The relationship between individual and group practice of the Transcendental Meditation and TM-Sidhi program and reductions in social stress, tension, and violence has been the topic of systematic exploration since the 1970s in Canada, India, Israel, Lebanon, New Zealand, Norway, the Philippines, Puerto Rico, United Kingdom, and United States. Findings from these quantitative studies have been published in leading international conflict resolution and peace studies journals. However, research in Cambodia has to date only been of a descriptive and qualitative nature with a focus on economic and social variables not violence or crime. The purpose of the present study is therefore to examine socio-political violence in Cambodia between January 1990 and December 1992 (the baseline period) and the possible influence of group practice of the Transcendental Meditation and TM-Sidhi program at Maharishi Vedic University (MVU) by 550 undergraduate students beginning in January 1993 through December 2008 (the impact-assessment period). This study uses an explanatory mixed methods design to examine socio-political violence using time series analysis of machine-coded news reports (quantitative data) and document analysis of national and international media reports, personal statements, and public documents (qualitative data). Results indicate that beginning in January 1993, when meditating students at MVU began their group practice, a marked downward shift occurred in the trends of socio-political violence and other forms of violent crime in Cambodia, contrary to predicted baseline trends and contrary to widespread community and media expectations. Such a conclusion can be drawn from both the quantitative and qualitative evidence when comparing baseline and impact-assessment periods, suggesting that the observed decline in socio-political violence during this time was associated with an increase in peace, order, and harmony—that is, a rise of social coherence—in the collective consciousness of Cambodia generated by the group of meditating students at MVU

Modelling of Human Control and Performance Evaluation using Artificial Neural Network and Brainwave

Conventionally, a human has to learn to operate a machine by himself / herself. Human Adaptive Mechatronics (HAM) aims to investigate a machine that has the capability to learn its operator skills in order to provide assistance and guidance appropriately. Therefore, the understanding of human behaviour during the human-machine interaction (HMI) from the machine’s side is essential. The focus of this research is to propose a model of human-machine control strategy and performance evaluation from the machine’s point of view. Various HAM simulation scenarios are developed for the investigations of the HMI. The first case study that utilises the classic pendulum-driven capsule system reveals that a human can learn to control the unfamiliar system and summarise the control strategy as a set of rules. Further investigation of the case study is conducted with nine participants to explore the performance differences and control characteristics among them. High performers tend to control the pendulum at high frequency in the right portion of the angle range while the low performers perform inconsistent control behaviour. This control information is used to develop a human-machine control model by adopting an Artificial Neural Network (ANN) and 10-time- 10-fold cross-validation. Two models of capsule direction and position predictions are obtained with 88.3% and 79.1% accuracies, respectively. An Electroencephalogram (EEG) headset is integrated into the platform for monitoring brain activity during HMI. A number of preliminary studies reveal that the brain has a specific response pattern to particular stimuli compared to normal brainwaves. A novel human-machine performance evaluation based on the EEG brainwaves is developed by utilising a classical target hitting task as a case study of HMI. Six models are obtained for the evaluation of the corresponding performance aspects including the Fitts index of performance. The averaged evaluation accuracy of the models is 72.35%. However, the accuracy drops to 65.81% when the models are applied to unseen data. In general, it can be claimed that the accuracy is satisfactory since it is very challenging to evaluate the HMI performance based only on the EEG brainwave activity

Classification of electroencephalogram (EEG) for lower limb movement of post stroke patients using artificial neural network (ANN)

Nowadays, many neurological conditions happen suddenly, such as stroke or spinal cord injury. This can cause chronic gait function impairment due to functional deficits in motor control. Current physiotherapy techniques such as functional electrical stimulation (FES) can be used to reconstruct some skills needed for movements of daily life. However, FES system provides only a limited degree of motor function recovery and has no mechanism for reflecting a patient’s motor intentions, hence requires novel therapies. Brain-Computer Interfaces (BCI) provides the means to decode mental states and activate devices according to user intentions. However, conventional BCI cannot be used fully, due to the lack of accuracy, and need some improvement. In addition to that, the integration of BCI with lower extremity FES systems has received less attention compared to the BCI-FES systems with upper extremity. The discussion of this thesis was divided into two parts, which were the BCI part as input and the functional electrical stimulator (FES) controller part as the output for this system. For BCI part, the main processes involved are brainwave signals classification and mapping process. Here the signal has been classed will be applied to match the appropriate rehabilitation exercise. Whereas for the FES part, the signal from the mapping system will be controlled by the controller to ensure that the target knee angle is achieved to make the rehabilitation process more effective. As a conclusion, patients can be classified into two classes based on their alpha and beta signals status and these must undergone rehabilitation sessions according to their post-stroke level. So the results proved that the ANN model developed was able to classify the post-stroke severity. Also, the result had proven that the BCI fuzzy-based mapping system in this study was able to work perfectly into mapping the post-stroke patient with a suitable exercise according to their post-stroke level

Sensor-based artificial intelligence to support people with cognitive and physical disorders

A substantial portion of the world's population deals with disability. Many disabled people do not have equal access to healthcare, education, and employment opportunities, do not receive specific disability-related services, and experience exclusion from everyday life activities. One way to face these issues is through the use of healthcare technologies. Unfortunately, there is a large amount of diverse and heterogeneous disabilities, which require ad-hoc and personalized solutions. Moreover, the design and implementation of effective and efficient technologies is a complex and expensive process involving challenging issues, including usability and acceptability. The work presented in this thesis aims to improve the current state of technologies available to support people with disorders affecting the mind or the motor system by proposing the use of sensors coupled with signal processing methods and artificial intelligence algorithms. The first part of the thesis focused on mental state monitoring. We investigated the application of a low-cost portable electroencephalography sensor and supervised learning methods to evaluate a person's attention. Indeed, the analysis of attention has several purposes, including the diagnosis and rehabilitation of children with attention-deficit/hyperactivity disorder. A novel dataset was collected from volunteers during an image annotation task, and used for the experimental evaluation using different machine learning techniques. Then, in the second part of the thesis, we focused on addressing limitations related to motor disability. We introduced the use of graph neural networks to process high-density electromyography data for upper limbs amputees’ movement/grasping intention recognition for enabling the use of robotic prostheses. High-density electromyography sensors can simultaneously acquire electromyography signals from different parts of the muscle, providing a large amount of spatio-temporal information that needs to be properly exploited to improve recognition accuracy. The investigation of the approach was conducted using a recent real-world dataset consisting of electromyography signals collected from 20 volunteers while performing 65 different gestures. In the final part of the thesis, we developed a prototype of a versatile interactive system that can be useful to people with different types of disabilities. The system can maintain a food diary for frail people with nutrition problems, such as people with neurocognitive diseases or frail elderly people, which may have difficulties due to forgetfulness or physical issues. The novel architecture automatically recognizes the preparation of food at home, in a privacy-preserving and unobtrusive way, exploiting air quality data acquired from a commercial sensor, statistical features extraction, and a deep neural network. A robotic system prototype is used to simplify the interaction with the inhabitant. For this work, a large dataset of annotated sensor data acquired over a period of 8 months from different individuals in different homes was collected. Overall, the results achieved in the thesis are promising, and pave the way for several real-world implementations and future research directions

Multi‑mechanical waves against Alzheimer’s disease pathology: a systematic review

Alzheimer’s disease (AD) is the most common cause of dementia, affecting approximately 40 million people worldwide. The ineffectiveness of the available pharmacological treatments against AD has fostered researchers to focus on alternative strategies to overcome this challenge. Mechanical vibrations delivered in different stimulation modes have been associated with marked improvements in cognitive and physical performance in both demented and nondemented elderly. Some of the mechanical-based stimulation modalities in efforts are earlier whole-body vibration, transcranial ultrasound stimulation with microbubble injection, and more recently, auditory stimulation. However, there is a huge variety of treatment specifications, and in many cases, conflicting results are reported. In this review, a search on Scopus, PubMed, and Web of Science databases was performed, resulting in 37 papers . These studies suggest that mechanical vibrations delivered through different stimulation modes are effective in attenuating many parameters of AD pathology including functional connectivity and neuronal circuit integrity deficits in the brains of AD patients, as well as in subjects with cognitive decline and non-demented older adults. Despite the evolving preclinical and clinical evidence on these therapeutic modalities, their translation into clinical practice is not consolidated yet. Thus, this comprehensive and critical systematic review aims to address the most important gaps in the reviewed protocols and propose optimal regimens for future clinical application.FCT (Fundação para a Ciência e Tecnologia) through the grant with reference SFRH/BD/09375/2020, and in the scope of the projects UIDB/04436/2020, UIDP/04436/2020, and NORTE-01- 0145-FEDER-000023, funded by the European Fund for Regional Development (FEDER) of the Operational Programme for Competitiveness and Internationalization (POCI), by Portugal 2020

Behavioral Biometric Security: Brainwave Authentication Methods

This paper investigates the possibility of creating an authentication system based on the measurements of the human brain. Discussed throughout will be an evaluation of the feasibility of brainwave authentication based on brain anatomy and behavior characteristics, conventional vs. dynamic authentication methods, the possibility of continuous authentication, and biometric ethical and security concerns.Bachelor of Scienc

Preferred music listening is associated with perceptual learning enhancement at the expense of self-focused attention

Can preferred music listening improve following attentional and learning performances? Here we suggest that this may be the case. In Experiment 1, following preferred and non-preferred musical-piece listening, we recorded electrophysiological responses to an auditory roving-paradigm. We computed the mismatch negativity (MMN – the difference between responses to novel and repeated stimulation), as an index of perceptual learning, and we measured the correlation between trial-by-trial EEG responses and the fluctuations in Bayesian Surprise, as a quantification of the neural attunement with stimulus informational value. Furthermore, during music listening, we recorded oscillatory cortical activity. MMN and trial-by-trial correlation with Bayesian surprise were significantly larger after subjectively preferred versus non-preferred music, indicating the enhancement of perceptual learning. The analysis on oscillatory activity during music listening showed a selective alpha power increased in response to preferred music, an effect often related to cognitive enhancements. In Experiment 2, we explored whether this learning improvement was realized at the expense of self-focused attention. Therefore, after preferred versus non-preferred music listening, we collected Heart-Beat Detection (HBD) accuracy, as a measure of the attentional focus toward the self. HBD was significantly lowered following preferred music listening. Overall, our results suggest the presence of a specific neural mechanism that, in response to aesthetically pleasing stimuli, and through the modulation of alpha oscillatory activity, redirects neural resources away from the self and toward the environment. This attentional up-weighting of external stimuli might be fruitfully exploited in a wide area of human learning activities, including education, neurorehabilitation and therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13423-022-02127-8