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

An EEG-based brain-computer interface for dual task driving detection

The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components. © 2013

Significant variables extraction of post-stroke EEG signal using wavelet and SOM kohonen

Stroke patients require a long recovery. One success of the treatment given is the evaluation and monitoring during recovery. One device for monitoring the development of post-stroke patients is Electroencephalogram (EEG). This research proposed a method for extracting variables of EEG signals for post-stroke patient analysis using Wavelet and Self-Organizing Map Kohonen clustering. EEG signal was extracted by Wavelet to obtain Alpha, beta, theta, gamma, and Mu waves. These waves, the amplitude and asymmetric of the symmetric channel pairs are features in Self Organizing Map Kohonen Clustering. Clustering results were compared with actual clusters of post-stroke and no-stroke subjects to extract significant variable. These results showed that the configuration of Alpha, Beta, and Mu waves, amplitude together with the difference between the variable of symmetric channel pairs are significant in the analysis of post-stroke patients. The results gave using symmetric channel pairs provided 54-74% accuracy

Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

A Review on Machine Learning Techniques for Neurological Disorders Estimation by Analyzing EEG Waves

With the fast improvement of neuroimaging data acquisition strategies, there has been a significant growth in learning neurological disorders among data mining and machine learning communities. Neurological disorders are the ones that impact the central nervous system (including the human brain) and also include over 600 disorders ranging from brain aneurysm to epilepsy. Every year, based on World Health Organization (WHO), neurological disorders affect much more than one billion people worldwide and count for up to seven million deaths. Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders. With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification. Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject