Advanced computational intelligence strategies for mental task classification using electroencephalography signals

University of Technology, Sydney. Faculty of Engineering and Information Technology.Brain-computer interface (BCI) has been known as a cutting-edge technology in the current research. It is able to measure the brain activity directly instead of using the natural peripheral nerves and muscles and translates the user’s intent brain activity into useful control signals. There is still a need for a technology for severely disabled individuals who suffer from locked-in syndromes, such as amyotrophic lateral sclerosis (ALS), cervical spinal cord injury (SCI) or tetraplegia and brain stem stroke. A brain-computer interface (BCI) could be used here as an alternative solution for control and communication. The main aim of this research is to develop a BCI system to assist mobility as hand-free technology for people with severe disability, with improved accuracy, which provides effective classification accuracy for wheelchair control. Electroencephalography (EEG) is the chosen BCI technology because it is non-invasive, portable and inexpensive. Currently, BCI using EEG can be divided into two strategies; selective attention and spontaneous mental signal. For the selective attention strategy, BCI relies on external stimuli which might be uncomfortable for severely disabled individuals who need to focus on external stimuli and the environment simultaneously. This is not the case for BCIs which rely on spontaneous mental signals initiated by the users themselves. BCI that uses sensorimotor rhythm (SMR) is one of the examples of the spontaneous mental strategy. There have been many reports in research using SMR-based BCI; however, there are still some people who are unable to use this. As a result, in this thesis, mental task-based EEG is used as an alternative. This thesis presents the embedded EEG system for mental task classification. A prototype wireless embedded EEG system for mental task BCI classification is developed. The prototype includes a wireless EEG as head gear and an embedded system with a wireless receiver. The developed wireless EEG provides a good common mode rejection ratio (CMRR) performance and a compact size with a low current consumption coin cell battery for power. Mental tasks data are collected using the prototype system from six healthy participants which include arithmetic, figure rotation, letter composing and counting task with additional eyes closed task. The developed prototype BCI system is able to detect the dominant alpha wave between 8-13Hz during eyes closed. Using the FFT as the features extractor and artificial neural network (ANN) as the classifier, the developed prototype EEG system provides high accuracy for the eyes closed and eyes open tasks. The classification of the three mental task combinations achieve an overall accuracy of around 70%. Also, an optimized BCI system for mental task classification using the Hilbert-Huang transform (HHT) feature extractor and the genetic algorithm optimization of the artificial neural network (GA-ANN) classifier is presented. Non motor imagery mental tasks are employed, including: arithmetic, letter composing, Rubik’s cube rolling, visual counting, ringtone, spatial navigation and eyes closed task. When more mental tasks are used, users are able to choose the most effective of tasks suitable for their circumstance. The result of classification for the three user chosen mental tasks achieves accuracy between 76% and 85% using eight EEG channels with GA-ANN (classifier) and FFT (feature extractor). In a two EEG channels classification using FFT as the features extractor, the accuracy is reduced between 65% and 79%. However, the HHT features extractor provides improved accuracy between 70% and 84%. Further, an advanced BCI system using the ANN with fuzzy particle swarm optimization using cross-mutated operation (FPSOCM-ANN) for mental task classification is presented. This experiment involves five able-bodied subjects and also five patients with tetraplegia as the target group of the BCI system. The three relevant mental tasks used for the BCI concentrates on mental letter composing, mental arithmetic and mental Rubik’s cube rolling forward. Although the patients group has lower classification accuracy, this is improved by increasing the time-window of data with the best at 7s. The results classification for 7s time-window show the best classifier is using the FPSOCM-ANN (84.4% using FPSOCM-ANN, 77.4% using GA-ANN, 77.0% using SVM, 72.1% using LDA, and 71.0% using linear perceptron). For practical use of a BCI, the two channels EEG is also presented using this advanced BCI classification method (FPSOCM-ANN). For overall, O1 and C4 are the best two channels at 80.5% of accuracy, followed by the second best at P3 and O2 at 76.4% of accuracy, and the third best at C3 and O2 channels at 75.4% of accuracy

Effective EEG analysis for advanced AI-driven motor imagery BCI systems

Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets.Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off

Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples.Spanish Ministerio de Ciencia, Innovacion y Universidades PGC2018-098813-B-C31 PGC2018-098813-B-C32 PSI201565848-

Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off

Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples

A distributed and energy‑efficient KNN for EEG classification with dynamic money‑saving policy in heterogeneous clusters

Universidad de Granada/CBUASpanish Ministry of Science, Innovation, and Universities under Grants PGC2018-098813-B-C31,PID2022-137461NB-C32ERDF fund. Funding for open access charge: University of Granada/ CBU

Adaptive Interactive Learning: a Novel Approach to Training Brain-Computer Interface Systems

Aju-arvuti liides (AAL) on süsteem, mis võimaldab infovahetust inimese aju ja arvuti vahel. Kasutades erinevaid neuropildistuste tehnikaid aju aktiivsust salvestatakse ja saadetakse arvutisse, kus signaal töödeldakse masinõpe meetoditega. AALi põhieesmärk on anda inimesele võimalust juhtida välisseadet kasutades mõttejõudu. Inimese mõtteseisundite eristame on raske ülesanne, mis ei ole lahendatav ainult masinõpe kasutamisega. Vastuvõetav klassifitseerimise täpsuse tase on saavutatav pärast pikajalist õpetamise protsessi, mille jooksul inimene õpib kuidas ta peab tekitama sobivad mõtteseisundid, ning arvuti loob mudeli, mis oskab neid eristada. Käesolevas töös me esitame uut lähenemist AAL süsteemi õpetamise protsessi jaoks. See põhineb inimese ja arvuti koostoimimise ideel, mille jooksul mõlemad osapooled adapteerivad oma käitumist vastavalt sellele, millist tagasisided nad saavad suhtlemise ajal. Pakutud viisi vastandiks on võetud traditsiooniline lähenemine, kus katseisik ei saa tagasisidet õppeprotsessi edukusest selle käigus. Teine uudsus traditsioonilise meetodiga võrreldes on juhendamata õppealgoritmi kasutamine (iseorganiseeriv kaart, SOM) meie süsteemi tuumana. Algne iseorganiseeruva kaardi algoritm on täiendatud niimoodi, et ta esindab tõenäosusliku ennustamise mudelit, mis oskab klassifitseerida aju signaali, anda tagasisidet katseisikule ning vajadusel kohandada mudelit reaalajas. Tuginedes läbiviidud eksperimentide tulemustel e järeldame, et interaktiivne lähenemine süsteemi õpetamiseks omab hulk eelisi traditsioonilise meetodiga võrreldes.A Brain-Computer Interface is a system which allows communication between a human and a computer. Using various neuroimaging techniques the brain activity is recorded and transmitted to the computer, where the signal is analyzed with the help of machine learning methods. The ultimate goal of BCI is to empower the human with the ability to control the external device with the power of thought. However, distinguishing mental states of a human is a challenging task and standard machine learning alone is not enough to solve the problem. Acceptable level of performance can be achieved after a long training process, during which the human learns how to produce suitable mental states and the machine creates a model, which is able to classify the signal. In this thesis we proposed a conceptually new approach to the process of training a BCI system. It relies on the idea of the interaction between the test subject and the machine and the ability of those two agents to adapt their behavior accordingly to the information they receive during the learning process. The approach is proposed as a counterpart to the traditional BCI training, where the test subject does not receive any feedback. Another novelty in comparison to the traditional approach is using an unsupervised learning algorithm (SOM) as the core of the learning system. The original concept of self-organizing maps is amended to represent a probabilistic predictive model, which can be used to classify the brain signal, provide feedback and adapt the model in real time. Based on the results of the conducted experiments we conclude that adaptive learning process has the multiple major advantages over the traditional one

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed