Square or Sine: Finding a Waveform with High Success Rate of Eliciting SSVEP

Steady state visual evoked potential (SSVEP) is the brain's natural electrical potential response for visual stimuli at specific frequencies. Using a visual stimulus flashing at some given frequency will entrain the SSVEP at the same frequency, thereby allowing determination of the subject's visual focus. The faster an SSVEP is identified, the higher information transmission rate the system achieves. Thus, an effective stimulus, defined as one with high success rate of eliciting SSVEP and high signal-noise ratio, is desired. Also, researchers observed that harmonic frequencies often appear in the SSVEP at a reduced magnitude. Are the harmonics in the SSVEP elicited by the fundamental stimulating frequency or by the artifacts of the stimuli? In this paper, we compare the SSVEP responses of three periodic stimuli: square wave (with different duty cycles), triangle wave, and sine wave to find an effective stimulus. We also demonstrate the connection between the strength of the harmonics in SSVEP and the type of stimulus

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

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

Processing resources and interplay among sensory modalities: an EEG investigation

The primary aim of the present thesis was to investigate how the human brain handles and distributes limited processing resources among different sensory modalities. Two main hypothesis have been conventionally proposed: (1) common processing resources shared among sensory modalities (supra-modal attentional system) or (2) independent processing resources for each sensory modality. By means of four EEG experiments, we tested whether putative competitive interactions between sensory modalities – regardless of attentional influences – are present in early sensory areas. We observed no competitive interactions between sensory modalities, supporting independent processing resources in early sensory areas. Consequently, we tested the influence of top-down attention on a cross-modal dual task. We found evidence for shared attentional resources between visual and tactile modalities. Taken together, our results point toward a hybrid model of inter-modal attention. Attentional processing resources seem to be controlled by a supra-modal attentional system, however, in early sensory areas, the absence of competitive interactions strongly reduces interferences between sensory modalities, thus providing a strong processing resource independence

Dual-Frequency SSVEP-based BCI for Reducing Eye Fatigue and Improving Classification Rate

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2016. 2. 박광석.The steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its high signal-to-noise ratio (SNR), and little requirement for training. However, the stimulus for evoking SSVEP causes high visual fatigue and has a risk of epileptic seizure. Furthermore, stimulation frequency is limited and the SSVEP amplitude diminishes when a monitor is used as a stimulator. In this thesis, a dual-frequency SSVEP is examined to resolve the aforementioned issues. Employing dual-frequency SSVEPs, two novel SSVEP-based BCIs are introduced to decrease eye fatigue and use harmonic frequencies with increased performance. First, the spectral characteristics of dual-frequency SSVEPs are investigated and frequency recognition methods for dual-frequency SSVEPs are suggested. Three methods based on power spectral density analysis (PSDA) and two methods based on canonical correlation analysis (CCA) were tested. The proposed CCA with a novel reference signal exhibited the best BCI performance, and the use of harmonic components improved the classification rate of the dual-frequency SSVEP. Second, the dual-frequency SSVEP response to an amplitude-modulated stimulus (AM-SSVEP) was explored to verify its performance with reduced eye fatigue. An amplitude-modulated stimulus was generated using the product of two sine waves at a carrier frequency (fc) and a modulating frequency (fm). The carrier frequency was higher than 40 Hz to reduce eye fatigue, and the modulating frequency ranged around the α-band (9–12 Hz) to utilize low-frequency harmonic information. The feasibility of AM-SSVEP with high BCI performance and low eye fatigue was confirmed through offline and online experiments. Using an optimized combination of the harmonic frequencies, the online experiments demonstrated that the accuracy of the AM-SSVEP was 97%, equivalent to that of the low-frequency SSVEP. Furthermore, subject evaluation indicated that an AM stimulus caused lower eye fatigue and less perception of flickering than a low-frequency stimulus, in a manner similar to a high-frequency stimulus. Third, a novel dual-frequency SSVEP-based hybrid SSVEP-P300 speller is introduced to overcome the frequency limitations and improve the performance. The hybrid speller consists of nine panels flickering at different frequencies. Each panel contains four different characters that appear in a random sequence. The flickering panel and the periodically updating character evoke the dual-frequency SSVEP, and the oddball stimulus of the target character evokes the P300. Ten subjects participated in offline and online experiments, in which accuracy and information transfer rate (ITR) were compared with those of conventional SSVEP and P300 spellers. The offline analysis revealed that the proposed speller elicited dual-frequency SSVEP. Moreover, the dual-frequency SSVEP significantly improved the SSVEP classification rate and ITR with a monitor in online experiments by 4 % accuracy and 18.8 bpm ITR. In conclusion, the proposed dual-frequency SSVEP-based BCIs reduce eye fatigue and improve SSVEP classification rate. The results indicate that this study provides a promising approach to make SSVEP-based BCIs more reliable and efficient for practical use.1. Introduction 1 1.1. Brain-Computer Interface 1 1.1.1. Basic Concepts 1 1.1.2. SSVEP-based BCIs 2 1.1.3. P300-based BCIs 5 1.1.4. Hybrid SSVEP-P300 BCIs 6 1.2. Motivation and Objectives 7 2. Frequency Recognition Methods for DFSSVEP-based BCI 11 2.1. Basic Concepts 11 2.2. DFSSVEP Recognition Methods 16 2.2.1. PSDA-based Methods 17 2.2.2. CCA-based Methods 20 2.3. Offline Analysis 23 2.3.1. Dual-Frequency Stimulus 23 2.3.2. Experimental Settings 24 2.3.3. Spectral Analysis of DFSSVEP 25 2.3.4. Signal Processing 26 2.4. Results 27 2.4.1. Harmonic Frequency 27 2.4.2. Comparison of Recognition Rates 28 2.5. Conclusion 31 3. DFSSVEP-based BCI for Reducing Eye Fatigue 33 3.1. Basic Concepts 33 3.1.1. Amplitude Modulation Technique 33 3.1.2. Amplitude-Modulated Stimuli for Evoking AM-SSVEP 35 3.2. Methods 38 3.2.1. Subjects and Experimental Settings 38 3.2.2. Offline Experiments 41 3.2.3. EEG Analysis 43 3.2.4. Online Experiments 45 3.3. Results 50 3.3.1. Harmonics of AM-SSVEP 50 3.3.2. Offline Analysis 54 3.3.3. CFC for Online Analysis 57 3.3.4. Online Analysis 59 3.3.5. Subject Evaluation 64 3.4. Discussion 66 3.4.1. Combining of Low- and High-Frequency SSVEPs 66 3.4.2. AM Harmonic Frequencies in CFC 70 3.4.3. Error Analysis 71 3.4.4. Effects of Environmental Illumination 74 3.5. Conclusion 76 4. DFSSVEP-based Hybrid BCI for Improving Classification Rate 79 4.1. Basic Concepts 79 4.2. Methods 85 4.2.1. Experimental Setting 85 4.2.2. Experimental Procedure 88 4.2.3. Signal Processing 89 4.2.4. Statistical Comparison of the EEG Responses 91 4.2.5. BCI Performance 92 4.3. Results 94 4.3.1. EEG Response to the Hybrid Speller 94 4.3.2. Offline Analysis 99 4.3.3. Online Analysis 102 4.4. Discussion 104 4.4.1. DFSSVEP 104 4.4.2. ITR Comparison with Conventional Spellers 109 4.4.3. ITR Comparison with Previous Studies 110 4.4.4. ITR with Different Visual Angle 114 4.4.5. Limitations 117 4.5. Conclusion 118 5. Conclusion 119 6. References 123 국문 초록 133Docto

Intrinsic and synaptic membrane properties of neurons in the thalamic reticular nucleus

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2004-2005Le noyau réticulaire thalamique (RE) est une structure qui engendre des fuseaux, une oscillation bioélectrique de marque pendant les stades précoces du sommeil. De multiples propriétés neuronales, intrinsèques et synaptiques, sont impliquées dans la génération, la propagation, le maintien et la terminaison des ondes en fuseaux. D’un autre côté, ce rythme constitue un état spécial de l’activité du réseau qui est généré par le réseau lui-même et affecte les propriétés cellulaires du noyau RE. Cette étude se concentre sur ces sujets: comment les propriétés cellulaires et les propriétés du réseau sont inter-reliées et interagissent pour engendrer les ondes fuseaux dans les neurones du RE et leurs cibles, les neurones thalamocorticaux. La présente thèse fournit de nouvelles évidences montrant le rôle fondamental joué par les neurones du noyau RE dans la genèse des ondes en fuseaux, dû aux synapses chimiques établies par ces neurones. La propagation et la synchronisation de l’activité sont modulées par les synapses électriques entre les neurones réticulaires thalamiques, mais aussi par les composantes dépolarisantes secondaires des réponses synaptiques évoquées par le cortex. De plus, la forme générale et la terminaison des oscillations thalamiques sont probablement contrôlées en grande partie par les neurones du RE, lesquels expriment une conductance intrinsèque leurs procurant une membrane avec un comportement bistable. Finalement, les oscillations thalamiques en fuseaux sont aussi capables de moduler les propriétés membranaires et l’activité des neurones individuels du RE.The thalamic reticular nucleus (RE) is a key structure related to spindles, a hallmark bioelectrical oscillation during early stages of sleep. Multiple neuronal properties, both intrinsic and synaptic, are implicated in the generation, propagation, maintenance and termination of spindle waves. On the other hand, this rhythm constitutes a special state of network activity, which is generated within, and affects single-cell properties of the RE nucleus. This study is focused on these topics: how cellular and network properties are interrelated and interact to generate spindle waves in the pacemaking RE neurons and their targets, thalamocortical neurons. The present thesis provides new evidence showing the fundamental role played by the RE nucleus in the generation of spindle waves, due to chemical synapses established by its neurons. The propagation and synchronization of activity is modulated by electrical synapses between thalamic reticular neurons, but also by the secondary depolarizing component of cortically-evoked synaptic responses. Additionally, the general shaping and probably the termination of thalamic oscillations could be controlled to a great extent by RE neurons, which express an intrinsic conductance endowing them with membrane bistable behaviour. Finally, thalamic spindle oscillations are also able to modulate the membrane properties and activities of individual RE neurons

Multiple Frequencies Sequential Coding for SSVEP-Based Brain-Computer Interface

BACKGROUND: Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has become one of the most promising modalities for a practical noninvasive BCI system. Owing to both the limitation of refresh rate of liquid crystal display (LCD) or cathode ray tube (CRT) monitor, and the specific physiological response property that only a very small number of stimuli at certain frequencies could evoke strong SSVEPs, the available frequencies for SSVEP stimuli are limited. Therefore, it may not be enough to code multiple targets with the traditional frequencies coding protocols, which poses a big challenge for the design of a practical SSVEP-based BCI. This study aimed to provide an innovative coding method to tackle this problem. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we present a novel protocol termed multiple frequencies sequential coding (MFSC) for SSVEP-based BCI. In MFSC, multiple frequencies are sequentially used in each cycle to code the targets. To fulfill the sequential coding, each cycle is divided into several coding epochs, and during each epoch, certain frequency is used. Obviously, different frequencies or the same frequency can be presented in the coding epochs, and the different epoch sequence corresponds to the different targets. To show the feasibility of MFSC, we used two frequencies to realize four targets and carried on an offline experiment. The current study shows that: 1) MFSC is feasible and efficient; 2) the performance of SSVEP-based BCI based on MFSC can be comparable to some existed systems. CONCLUSIONS/SIGNIFICANCE: The proposed protocol could potentially implement much more targets with the limited available frequencies compared with the traditional frequencies coding protocol. The efficiency of the new protocol was confirmed by real data experiment. We propose that the SSVEP-based BCI under MFSC might be a promising choice in the future

Sinc-Windowing and Multiple Correlation Coefficients Improve SSVEP Recognition Based on Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) is an increasingly used approach in the field of Steady-State Visually Evoked Potential (SSVEP) recognition. The efficacy of the method has been widely proven, and several variations have been proposed. However, most CCA variations tend to complicate the method, usually requiring additional user training or increasing computational load. Taking simple procedures and low computational costs may be, however, a relevant aspect, especially in view of low-cost and high-portability devices. In addition, it would be desirable that the proposed variations are as general and modular as possible to facilitate the translation of results to different algorithms and setups. In this work, we evaluated the impact of two simple, modular variations of the classical CCA method. The variations involved (i) the number of canonical correlations used for classification and (ii) the inclusion of a prefiltering step by means of sinc-windowing. We tested ten volunteers in a 4-class SSVEP setup. Both variations significantly improved classification accuracy when they were used separately or in conjunction and led to accuracy increments up to 7-8% on average and peak of 25\u201330%. Additionally, variations had no (variation (i)) or minimal (variation (ii)) impact on the number of algorithm steps required for each classification. Given the modular nature of the proposed variations and their positive impact on classification accuracy, they might be easily included in the design of CCA-based algorithms that are even different from ours

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 143

This supplement to Aerospace Medicine and Biology (NASA SP-7011) lists 251 reports, articles and other documents announced during June 1975 in Scientific and Technical Aerospace Reports (STAR) or in International Aerospace Abstracts (IAA). The first issue of the bibliography was published in July 1964; since that time, monthly supplements have been issued. In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, and environmental effects to which man is subjected during and following simulated or actual flight in the earth's atmosphere or in interplanetary space. References describing similar effects of biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. In general, emphasis is placed on applied research, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion

Epileptogenesis in rodents leads to neural system dysfunction and loss of associative memory measured by auditory event related potentials.

Epilepsy is a common and disabling neurological condition affecting 1-2% of the world’s population. Individuals suffering from epilepsy are prone to cognitive dysfunctions with detrimental effects in neural processing and memory resulting in decreases in quality of life. An evaluation of inherent neural processes is valuable information to diagnose and clinically assess cognitive function, which could significantly improve the treatment possibilities and thereby the quality of life for epilepsy patients. An evaluation of cognitive functions during epileptogenesis was performed by experiments using auditory event related potentials (ERP) in rats before and after induction of status epilepticus (SE) using the Lithium-Pilocarpine model (LP) of epilepsy. The aim of this study was to assess changes in neural system function during epileptogenesis by evaluating inherent responses to auditory stimuli in three ERP tasks at different time periods: before SE (control state), one week-, one month- and two months- after SE (epileptic state). 1. Habituation- (a.) evaluate the ability to habituate to repeated auditory stimuli using the N70 peak response, (b.) examine the time-frequency response through inter-trial coherence (ITC) and event-related spectral perturbation (ERSP); 2. Chirp- evaluate the auditory steady state responses through ITC; and, 3. Mismatch-Negativity (MMN)- evaluate associative memory through ERP responses to regular or odd tones. Habituation tasks showed increased N70 peak magnitude during epileptogenesis from 1-week, 1-month, and 2-months after SE using repeated measures analysis of variance (rANOVA) with significant differences before and after SE (p\u3c0.05, 1-week, 2-months). ITC showed significant differences between groups during habituation from 0.5-20 Hz and ERSP from 60-100 Hz and 0.5-15 Hz, with baseline corrected ERSP revealing differences from 1-30 Hz. The habituation results indicate a diminished ability to properly habituate to repeated stimuli with abnormal neuronal firing in the epileptic state compared to the non-epileptic control state linking a possible mechanism with imbalances in neuronal inhibition and excitation during epileptogenesis. Chirp response ITC showed increased synchronous activity in high gamma band (\u3e40 Hz) during epileptogenesis indicating the neuronal response in epileptic groups are phase locked to the chirp stimuli at a higher incidence than controls. Epileptic MMN ERP responses for odd and regular tones exhibited a decrease in the response curves from 250-350ms post-stimulus indicating a loss of ability to distinguish tones and difficulties with their associative memory during epileptogenesis.Our results indicate that a proper EEG-based analysis of auditory ERPs are useful in evaluating neural systems during epileptogenesis showing clear imbalances in excitatory: inhibitory function, as well as an indication that associative memory is detrimentally affected. The ERP methods employed may help in the diagnosis of the epileptic patients with cognitive disabilities as their epilepsy progresses, as it is simple, non-invasive and cost effective