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1

Performance of a matching pursuit based classifier on vep: a potential parameter for assessment of the state of patients with disorders of consciousness

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Dovgialo M., Chabuda A., Duszyk A., Pietrzak M., Rozanski P., Zieleniewska M., Durka P. J.
Acta Neurobiologiae Experimentalis
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2017
|
tom 77
|
nr Suppl.1
INTRODUCTION: P300 event‑related potential reflects the brain response to external stimuli. Attention paid to one of many repeatedly presented stimuli can be detected from the relative strengths of the responses; if the subject actively counts the occurrences of one of the stimuli (target), specific waveform responsе, namely the P300 potential, may be observed, and it will be absent during the unattended (non‑target) stimuli. This difference can be used as an indicator of conscious information processing in unresponsive patients suffering from disorders of consciousness (DoC). However, P300 waveforms recorded from those patients may significantly differ from the classical shape known from healthy subjects. AIM(S): We test the possibility of replacing the classical indicators used for assessing the difference between responses to target and non-target stimuli by cross-validation of a classifier detecting responses, based upon multivariate matching pursuit (MMP) parameterization. METHOD(S): Visual P300 potentials were recorded in a standard paradigm from a group of healthy subjects and patients in different states of disorder of consciousness. MMP algorithm was used as parametrization, and based upon a subset of recorded data a classifier was trained to distinguish responses to target and non-target stimuli. RESULTS: Cross‑validation performance of the classifier measured as the area under corresponding ROC curves discriminates the healthy group from DoC patients, and in some cases correlates with the severity of DoC. CONCLUSIONS: Replacing estimation of the statistical significance of the average P300 amplitudes by the performance of MMP‑based classification assessed by cross‑validation allows for nonparametric detection of conscious responses in DoC patients, whose ERPs do not always exhibit the classical components. FINANCIAL SUPPORT: This research was supported by the Polish National Science Centre grant 2015/17/B/ ST7/03784.
2

Brain-computer interfaces and disorders of consciousnes

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Durka P. J., Chabuda A., Dovgialo M., Duszyk A., Pietrzak M., Rozanski P., Zieleniewska M.
Acta Neurobiologiae Experimentalis
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2017
|
tom 77
|
nr Suppl.1
We will start with a brief introduction of the state of the art in EEG-based brain-computer interfaces (BCIs). Similar technologies, that is experimental paradigms and signal processing methods derived from the field of BCI, are believed to be promising candidates to solve one of the major problems of contemporary neuroscience, which is the lack of a stable method for assessment of patients with disorders of consciousness (DoC), commonly (and not quite correctly) addressed as “coma”. This situation motivates the research project started recently in cooperation with a model hospital for children with severe brain damage (Warsaw’s “Alarm Clock Clinic”). In the second part of this talk we will briefly present some of the preliminary results. P300 event-related potential is the classical electroenceph alographic indicator of conscious information processing. It occurs as a component in the EEG trials synchronized to those stimuli, that the subject was paying attention to, e.g. counting. In the standard P300-BCI paradigm, concentration on one of the subsequently flashing stimuli can be used as a conscious choice of one of the options, allowing for non-muscular transfer of information directly from the brain. If reliably detected in a DoC patient in response to the stimulus that the patient was asked to count, it proves the patient’s ability to understand and follow commands. Also, it offers a possibility of establishing a non‑muscular communication channel. A similar reasoning proves the usefulness of detection of the movement imagery reflections in EEG. Finally, brain’s recovery can be also reflected in regaining the sleep pattern known from healthy subjects, also observable in EEG recordings. Detailed presentations of preliminary results from these approaches will be available in the poster session. FINANCIAL SUPPORT: This research was supported by the Polish National Science Centre grant 2015/17/B/ ST7/03784.
3

Classification of time-frequency EEG responses of patients with disorders of consciousness

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Chabuda A., Rozanski P., Dovgialo M., Duszyk A., Pietrzak M., Zieleniewska M., Durka P. J.
Acta Neurobiologiae Experimentalis
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2017
|
tom 77
|
nr Suppl.1
INTRODUCTION: Event-related (de-)synchronization (ER(D)S) is a short‑lasting modulation of specific frequency bands (e.g. alpha band) of EEG, which occurs in response for external stimuli (visual, haptic) or with motor imagery and execution of movements. Since it’s both time- and frequency‑specific, it is typically analysed in time‑frequency space, using Fourier or wavelet methods. AIM(S): As part of the research project of University of Warsaw’s Faculty of Physics and Warsaw’s “Alarm Clock Clinic” (Klinika Budzik), we try to assess whether the presence of a ER(D)S in EEG can be used as an indicator of consciousness in DOC (disorders of consciousness) patients. METHOD(S): Results from two paradigms are presented: 1) motor imagery experiments consisting of a series of auditory commands (“move your hand/leg”) and, 2) haptic stimulation sessions of vibrations applied to patient’s shoulder/hand, while the patient was instructed to focus on stimuli delivered to given location. EEG (23 electrodes from extended 10-20 system) and EMG signals were recorded. EEG data were analysed in time-frequency space to identify whether any statistically significant ER(D)S had occurred. RESULTS: Assessments of possible conscious responses reflected in EEG were correlated with the patients’ CRS‑R (Coma Recovery Scale-Revised) diagnosis and, in case of ER(D)S, with corresponding EMG signal. We present results of several possible indicators, based both on the statistical significance of time‑frequency features, as well as on the cross‑validated classification accuracy. CONCLUSIONS: While the major problem we encountered was caused by severe contamination of EEG with involuntary movement artifacts, we noted that EEG patterns of DOC patients are far from uniform, which is related not only to the patients’ neurological state, but also to physical skull defects and reconstructions. Instead of the classical approach of comparing the patterns of patients to the control group, we propose to look for any statistically stable traces of conscious responses. FINANCIAL SUPPORT: This research was supported by the Polish National Science Centre grant 2015/17/B/ ST7/03784.
4

Successful BCI communication via high‑frequency SSVEP or visual, audio or tactile P300 in 30 tested volunteers

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Chabuda A., Dovgialo M., Duszyk A., Stroz A., Pawlisz M., Durka P.
Acta Neurobiologiae Experimentalis
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2019
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tom 79
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nr 4
In the pursuit to clarify the concept of “BCI illiteracy”, we investigated the possibilities of attaining basic binary (yes/no) communication via brain‑computer interface (BCI). We tested four BCI paradigms: steady‑state visual evoked potentials (SSVEP), tactile, visual, and auditory evoked potentials (P300). The proposed criterion for assessing for the possibility of communication are based on the number of correct choices obtained in a given BCI paradigm after a short calibration session, without prior training. In this study users answered 20 simple “yes/no” questions. Fourteen or more correct answers rejected the null hypothesis of random choices at P=0.05. All of the 30 healthy volunteers were able to attain above‑chance choices in at least one of the four paradigms. Additionally, we tested the system in clinical settings on a patient recovering from disorders of consciousness, achieving successful communication in 2 out of 3 paradigms. In light of these facts, after a review of the sparse literature, and in the interest of motivating further research, we propose a paraphrase of de Finetti’s provocative statement: “BCI illiteracy does not exist”.
5

Recent advances in Brain-Computer Interfaces from braintech.pl

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Chabuda A., Dovgialo M., Rozanski P., Wieteska M., Pawlisz M., Duszyk A., Zygierewicz J., Rudkiewicz T., Durka P.
Acta Neurobiologiae Experimentalis
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2018
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tom 78
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nr Suppl.1
Brain Computer Interface (BCI) is a system that al‑ lows communication without the mediation of muscles, using only brain waves. This technology passed from science-fiction to the laboratory decades ago, but real world applications, in fields from gaming and military to assistive technologies and consciousness assess‑ ment, are still operating at the proof of concept level. To change this landscape, building on a strong academic background, BrainTech Ltd. (http://braintech.pl) is pur‑ suing a project to create stable, robust, and usable BCI technologies, ready for the above mentioned real world applications. Software includes stable implementations of the major paradigms: P300 evoked potentials (visu‑ al and auditory), steady-state visual evoked potentials (SSVEP), and motor imagery. Features aimed at increas‑ ing productivity in both academic and practical appli‑ cations include the “BCI Control Panel”(*), which helps either the experimenter or caregiver in the setup and online control of the BCI session by displaying, for ex‑ ample, electrode impedances and online performance. Several indicators like accuracy or information transfer rate can be stored together with the signal, facilitating offline scientific analysis. Hardware systems include: (1) comfortable headcap with water-based electrodes, offering high quality signal without application of con‑ ducting gel, which normally requires washing hair after each EEG session, (2) 8-channel 24-bit wireless EEG am‑ plifier, offering online monitoring of electrode contacts and Gigaohm input impedance, either integrated into the headcap or offering connectors for standard EEG electrodes, (3) next generation of the “BCI Appliance”* – a dedicated hardware renderer for flexible stimuli for high-frequency SSVEP, first presented at CeBIT in 2012 as a base for the fastest BCI presented at this fair. (*“BCI Control Panel” and “BCI Appliance” are trademarks filed for protection to the Polish Patent Office). Demo of the discussed systems will be available in the presentation accompanying the Conference. The lecture will briefly discuss new research possibilities opened by the “BCI Appliance”, comfort of application of the novel EEG headcap, and facilitation of both real world BCI applica‑ tions and scientific research brought about by the pre‑ sented software.
6

Overnight eeg profiles in monitoring the process of regaining consciousness

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Zieleniewska M., Piotrowski T., Duszyk A., Piwonski K., Chabuda A., Dovgialo M., Pietrzak M., Rozanski P., Durka P. J.
Acta Neurobiologiae Experimentalis
|
2017
|
tom 77
|
nr Suppl.1
INTRODUCTION: Previous research on disorders of consciousness (DOC) phenomena indicated significant changes in circadian activity and sleep architecture that correlated with patient’s diagnosis. Although polysomnography seems to provide a valuable tool in assessing consciousness level, the main obstacle is the absence of specific staging criteria in scoring sleep patterns of patients with DOC and the inaccuracy of neuropsychological diagnosis. AIM(S): The aim of the study was to identify potential quantitative EEG indices in polysomnographic sleep patterns in patients regaining consciousness with main focus on slow wave activity regulation (SWA) and sleep spindles. Besides visual scoring of PSG recordings, we also performed an automatic SWA and sleep spindles detection and parametrization based on the matching pursuit (MP) algorithm. METHOD(S): Preliminary results of one MCS patient are presented. Overnight multichannel EEG recordings and neuropsychological examination with Coma Recovery Scale-Revised (CRS-R) were performed every 2 months during patient’s one-year stay in a model hospital for children with severe brain damages. Each recording was visually scored by an expert with modified AASM sleep scoring criteria, adjusted to specific characteristics of pediatric DOC sleep patterns. We also performed automatic analysis of EEG sleep profiles based on the MP algorithm. RESULTS: Overall, the overnight EEG profiles of SWA and sleep spindles correlated with visual scores and neuropsychological assessment with CRS-R. Apart from that, for one patient (whose data are hereby presented), some of the SWA and sleep spindles characteristics preceded improvement in the CRS‑R diagnosis. These effects were not clearly detectable in the visual assessment of the polysomnograms. CONCLUSIONS: Preliminary results indicate that automatic parametrization of sleep structures, obtained from the MP algorithm, might provide a valuable tool in monitoring patient’s consciousness level during the rehabilitation process. FINANCIAL SUPPORT: This research was supported by the Polish National Science Centre grant 2015/17/N/ ST7/03769.
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