An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI

An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI

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

An Adaptive Task-Related Component Analysis Method for SSVEP recognition

Steady-state visual evoked potential (SSVEP) recognition methods are equipped with learning from the subject's calibration data, and they can achieve extra high performance in the SSVEP-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This study develops a new method to learn from limited calibration data and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEPs detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, an multitask learning approach, based on the bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperform competing methods by a significant margin.Comment: 23 pages, 3 Figures, 6 Table

Machine Learning in VEP-based BCI

Antud töös esitatakse visuaalse stiimuliga esilekutsutud potentsiaalidel põhineva ajuarvuti liidese (AAL) jaoks klassifitseerimisreegel, mis põhineb tunnuste ja lävendväärtuste omavahelisel võrdlusel. Klassifitseerimise jaoks optimaalsete lävendväärtuste leidmine formaliseeritakse maksimeerimisülesandena, kus maksimeeritakse AALi informatsiooniedastamise kiirus, mille arvutamiseks tuletatakse eraldi valem, et vältida standardse valemi poolt vajalikke eeldusi. Esitatud reegel näitab AALi klassifitseerimisülesandes häid tulemusi, saavutades informatsiooni edastamise kiiruseks kuni 60 bitti minutis. Samuti võimaldab pakutud reegel vältida vale-ennustusi, mis on oluline AALi kasutamiseks igapäevaelus. AALid omavad suurt potentsiaali medistsiini valdkonnas, kuna võimaldavad raske puudega või halvatud isikutel seadmeid kontrollida.In this thesis, a classification method for SSVEP-based BCI is proposed. The classification method is based on simple comparisons of extracted feature values and thresholds and it involves a way of optimising the thresholds. Optimising the thresholds is formalised as a maximisation task of the information transfer rate of BCI, but instead of using the standard formula for calculating ITR, more general formula is derived. This allows the thresholds to be automatically optimised and avoids calculating incorrect ITR estimate.The proposed method shows good performance in classifying targets of a BCI and achieves ITR as high as 60 bit/min. The proposed method also provides a way to reduce false classifications, which is important in real-world applications. BCIs have high potential to be used in the field of medicine as they provides a way for severely disabled people to control external devices

Control a Robot via VEP Using Emotiv EPOC

Antud töö kirjeldab visuaalse stiimuliga esilekutsutud potentsiaalidel põhinevat aju ning arvuti vahelist liidest (AAL), mis loodi antud töö praktilise osana. AALi saab kasutada aju ja seadme vahelise otsese suhtluskanali loomiseks, mis tähendab, et seadmega suhtlemiseks pole vaja nuppe vajutada, piisab vaid visuaalsete stiimulite vaatamisest. Efektiivne AAL võimaldaks raske puudega isikutel näiteks elektroonilist ratastooli juhtida. Antud töö osana loodud AAL kasutab tuntud kanoonilise korrelatsiooni- ja võimsusspektri analüüsi meetodeid ning uuendusena kombineerib need kaks meetodit üheks teineteist täiendavaks meetodiks. Kahe meetodi kombinatsioon muudab AALi täpsemaks. AALi testiti antud töös vaid pealiskaudselt ning tulemused on järgnevad: ühe käsu edastamise aeg 2,61 s, täpsus 85,81% ning informatsiooni edastamise kiirus 27,73 bitt/min. Antud AAL on avatud lähtekoodiga, kirjutatud Python 2.7 programmeerimiskeeles, sisaldab graafilist kasutajaliidest ning kasutab aju tegevuse mõõtmiseks elektroensefalograafia (EEG) seadet Emotiv EPOC. AALi kasutamiseks on vaja ainult arvutit ja Emotiv EPOC seadet. Koodi muutes on võimalik kasutada ka teisi EEG seadmeid.This thesis describes an SSVEP-based BCI implemented as a practical part of this work. One possible usage of a BCI that efficiently implements a communication channel between the brain and an external device would be to help severely disabled people to control devices that currently require pushing buttons, for example an electric wheelchair. The BCI implemented as a part of this thesis uses widely known PSDA and CCA feature extraction methods and introduces a new way to combine these methods. Combining different methods improves the performance of a BCI. The application was tested only superficially and the following results were obtained: 2.61 s target detection time, 85.81% accuracy and 27.73 bits/min ITR. The implemented BCI is open-source, written in Python 2.7, has graphical user interface and uses inexpensive EEG device called Emotiv EPOC. The BCI requires only a computer and Emotiv EPOC, no additional hardware is needed. Different EEG devices could be used after modifying the code

Development of a practical and mobile brain-computer communication device for profoundly paralyzed individuals

Thesis (Ph.D.)--Boston UniversityBrain-computer interface (BCI) technology has seen tremendous growth over the past several decades, with numerous groundbreaking research studies demonstrating technical viability (Sellers et al., 2010; Silvoni et al., 2011). Despite this progress, BCIs have remained primarily in controlled laboratory settings. This dissertation proffers a blueprint for translating research-grade BCI systems into real-world applications that are noninvasive and fully portable, and that employ intelligent user interfaces for communication. The proposed architecture is designed to be used by severely motor-impaired individuals, such as those with locked-in syndrome, while reducing the effort and cognitive load needed to communicate. Such a system requires the merging of two primary research fields: 1) electroencephalography (EEG)-based BCIs and 2) intelligent user interface design. The EEG-based BCI portion of this dissertation provides a history of the field, details of our software and hardware implementation, and results from an experimental study aimed at verifying the utility of a BCI based on the steady-state visual evoked potential (SSVEP), a robust brain response to visual stimulation at controlled frequencies. The visual stimulation, feature extraction, and classification algorithms for the BCI were specially designed to achieve successful real-time performance on a laptop computer. Also, the BCI was developed in Python, an open-source programming language that combines programming ease with effective handling of hardware and software requirements. The result of this work was The Unlock Project app software for BCI development. Using it, a four-choice SSVEP BCI setup was implemented and tested with five severely motor-impaired and fourteen control participants. The system showed a wide range of usability across participants, with classification rates ranging from 25-95%. The second portion of the dissertation discusses the viability of intelligent user interface design as a method for obtaining a more user-focused vocal output communication aid tailored to motor-impaired individuals. A proposed blueprint of this communication "app" was developed in this dissertation. It would make use of readily available laptop sensors to perform facial recognition, speech-to-text decoding, and geo-location. The ultimate goal is to couple sensor information with natural language processing to construct an intelligent user interface that shapes communication in a practical SSVEP-based BCI

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

Objective: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain–computer interface (BCI) applications. Approach: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results: We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance: The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI

An SSVEP Brain-Computer Interface: A Machine Learning Approach

A Brain-Computer Interface (BCI) provides a bidirectional communication path for a human to control an external device using brain signals. Among neurophysiological features in BCI systems, steady state visually evoked potentials (SSVEP), natural responses to visual stimulation at specific frequencies, has increasingly drawn attentions because of its high temporal resolution and minimal user training, which are two important parameters in evaluating a BCI system. The performance of a BCI can be improved by a properly selected neurophysiological signal, or by the introduction of machine learning techniques. With the help of machine learning methods, a BCI system can adapt to the user automatically. In this work, a machine learning approach is introduced to the design of an SSVEP based BCI. The following open problems have been explored: 1. Finding a waveform with high success rate of eliciting SSVEP. SSVEP belongs to the evoked potentials, which require stimulations. By comparing square wave, triangle wave and sine wave light signals and their corresponding SSVEP, it was observed that square waves with 50% duty cycle have a significantly higher success rate of eliciting SSVEPs than either sine or triangle stimuli. 2. The resolution of dual stimuli that elicits consistent SSVEP. Previous studies show that the frequency bandwidth of an SSVEP stimulus is limited. Hence it affects the performance of the whole system. A dual-stimulus, the overlay of two distinctive single frequency stimuli, can potentially expand the number of valid SSVEP stimuli. However, the improvement depends on the resolution of the dual stimuli. Our experimental results shothat 4 Hz is the minimum difference between two frequencies in a dual-stimulus that elicits consistent SSVEP. 3. Stimuli and color-space decomposition. It is known in the literature that although low-frequency stimuli (\u3c30 Hz) elicit strong SSVEP, they may cause dizziness. In this work, we explored the design of a visually friendly stimulus from the perspective of color-space decomposition. In particular, a stimulus was designed with a fixed luminance component and variations in the other two dimensions in the HSL (Hue, Saturation, Luminance) color-space. Our results shothat the change of color alone evokes SSVEP, and the embedded frequencies in stimuli affect the harmonics. Also, subjects claimed that a fixed luminance eases the feeling of dizziness caused by low frequency flashing objects. 4. Machine learning techniques have been applied to make a BCI adaptive to individuals. An SSVEP-based BCI brings new requirements to machine learning. Because of the non-stationarity of the brain signal, a classifier should adapt to the time-varying statistical characters of a single user\u27s brain wave in realtime. In this work, the potential function classifier is proposed to address this requirement, and achieves 38.2bits/min on offline EEG data