EEG Biomarkers Related With the Functional State of Stroke Patients

Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps. The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (ΔFMA = 1 median [IQR: 0-8], P = 0.002). The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses

Brain computer interfaces for brain acquired damage

El terme Interfície Cervell-Ordinador (ICC), va sorgir als anys 70 pel Dr. Jacques J. Vidal, que mitjançant l’ús de l’electroencefalografia (EEG) fou el primer a intentar proporcionar una sortida alternativa als senyals cerebrals per controlar un dispositiu extern. L’objectiu principal d’aquesta fita era ajudar als pacients amb problemes de moviment i comunicació a relacionar-se amb el seu entorn. Des de llavors, molts neurocientífics han emprat aquesta idea i han intentat posar-la en pràctica utilitzant diferents mètodes d’adquisició i processament del senyal, nous dispositius d’interacció, noves metes i objectius. Tot això ha facilitat l’aplicació d’aquesta tecnologia en moltes àrees, i actualment les ICC s’utilitzen per jugar a videojocs, moure cadires de rodes, facilitar l’escriptura en persones sense mobilitat, definir criteris i preferències en el món del comerç i el consum, o inclús poden servir com a detector de mentides. Tot i així, el sector que presenta un major avenç en el desenvolupament de les ICC, és el sector biomèdic. A grans trets, podem utilitzar les ICC amb dues finalitats diferents dins de la neurorehabilitació; substituint una funció perduda o induint canvis en la plasticitat neuronal amb l’objectiu de restaurar o compensar la funció perduda. Existeixen diferents principis per al registre dels senyals del cervell; de manera invasiva, col·locant els elèctrodes de registre dintre de la cavitat cranial, o de manera no invasiva, col·locant els elèctrodes de registre fora de la cavitat cranial. El mètode més conegut i difós és l’EEG. El seu ús és molt adequat en entorns clínics, té una resolució temporal molt precisa i és possible obtenir una retroalimentació en temps real que pot induir la plasticitat cortical i el restabliment de la funció motora normal. En aquesta tesi presentem tres objectius diferents: (1) avaluar els afectes clínics de la rehabilitació mitjançant les ICC en pacients amb ictus, ja sigui realitzant un meta-anàlisi dels estudis publicats o avaluant els canvis funcionals dels pacients amb ictus després de la teràpia d’ICC; (2) explorar paràmetres alternatius per quantificar els efectes de les ICC en pacients amb ictus, avaluant diferents biomarcadors de l’EEG en pacients amb aquesta patologia i correlacionant aquests marcadors amb els resultats de les escales funcionals; (3) optimitzar el sistema ICC mitjançant la gamificació d’un avatar.El término Interfaz Cerebro-Computadora (ICC) surgió en los años 70 por el Dr. Jacques J. Vidal, que mediante el uso de la electroencefalografía (EEG) trató de dar una salida alternativa a las señales del cerebro para controlar un dispositivo externo. El objetivo principal de esta hazaña era ayudar a los pacientes con problemas de movimiento o comunicación a relacionarse con el entorno. Desde entonces, muchos neurocientíficos han utilizado esta idea y han tratado de ponerla en práctica utilizando diferentes métodos de adquisición y procesamiento de señales, nuevos dispositivos de interacción y nuevas metas y objetivos. Todo ello ha facilitado la aplicación de esta tecnología en muchas áreas y actualmente las ICC se utilizan para jugar a videojuegos, mover sillas de ruedas, facilitar la escritura en personas sin movilidad, establecer criterios y preferencias de compra en el mundo del comercio y el consumo, o incluso pueden servir como detector de mentiras. Sin embargo, el sector que presenta un mayor avance y desarrollo de las ICC es el sector biomédico. A grandes rasgos podemos utilizar las ICC con dos finalidades distintas dentro de la neurorehabilitación; sustituir una función perdida o inducir cambios en la plasticidad neuronal con el objetivo de restaurar o compensar dicha función perdida. Hay diferentes principios para el registro de las señales del cerebro; de forma invasiva, colocando los electrodos de registro dentro de la cavidad craneal, o no invasiva, colocando los electrodos de registro fuera de la cavidad craneal. El método más conocido y difundido es la EEG. Su uso es adecuado para entornos clínicos, tiene una resolución temporal muy precisa y su retroalimentación en tiempo real puede inducir la plasticidad cortical y el restablecimiento de la función motora normal. En esta tesis presentamos tres objetivos diferentes: (1) evaluar los efectos clínicos de la rehabilitación mediante las ICC en pacientes con ictus, ya sea realizando un meta-análisis de los estudios publicados o evaluando los cambios funcionales en los pacientes con ictus después de la terapia de ICC; (2) explorar parámetros alternativos para cuantificar los efectos de las ICC en pacientes con ictus, evaluando diferentes biomarcadores de electroencefalografía en pacientes con esta patología y correlacionando los posibles cambios en estos parámetros con los resultados en las escalas funcionales; (3) optimizar el sistema ICC utilizando mediante la gamificación de un avatar.The term Brain Computer Interface (BCI) emerged in the 70's by Dr. Jacques J Vidal, who by using electroencephalography (EEG) tried to give an alternative output to the brain signals in order to control an external device. The main objective of this feat was to help patients with impaired movement or communication to relate themselves to the environment. Since then many neuroscientists have used this idea and have tried to implement it using different methods of signal acquisition and processing, new interaction devices, new goals and objectives. All this has facilitated the implementation of this technology in many areas and currently BCI is used to play video games, move wheelchairs, facilitate writing in people without mobility, establish criteria and purchase preferences in the world of marketing and consumption, or even serve as a lie detector. However, the sector that presents the most marked progress and development of BCI is the biomedical sector. In rough outlines we can use BCI with two different purposes within the neurorehabilitation; to substitute a lost function or to induce neural plasticity changes with the aim to restore or compensate the lost function. To restore a lost function by inducing neuroplastic changes in the brain is undoubtedly a challenging strategy but a feasible goal through BCI technology. This type of intervention requires that the patient invests time and effort in a therapy based on the practice of motor image and feedback mechanisms in real time. There are different principles to record the brain signals; invasively, placing the recording electrodes inside the cranial cavity, or non-invasive, placing the recording electrodes outside of the cranial cavity. The best known and most widespread one is EEG, since they are suitable for clinical environments, have a highly accurate temporal resolution and their real-time feedback can induce cortical plasticity and the restoration of normal motor function. On this thesis we present three different objectives: (1) to evaluate the clinical effects of rehabilitation based on BCI system in stroke patients, either by performing a meta-analysis of published studies or by evaluating functional changes in stroke patients after BCI training; (2) to explore alternative parameters to quantify effects of BCI in stroke patients, by evaluating different electroencephalography biomarkers in stroke patients and correlating potential changes in these parameters with functional scales; (3) to optimize the BCI system by using a new gamified avatar.Universitat Autònoma de Barcelona. Programa de Doctorat en Neurocièncie

Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system

IntroductionBrain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions.MethodsPatients’ functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively.ResultsPatients’ motor function as assessed by the FMA-UE improved by an average of 4.2 points (p &lt; 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed.DiscussionThe results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions

Corrigendum : EEG biomarkers related with the functional state of stroke patients

[This corrects the article DOI: 10.3389/fnins.2020.00582.]

Real-time estimation of EEG-based engagement in different tasks.

Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Real-time estimation of EEG-based engagement in different tasks

Objective. Recent trends in brain–computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG). Approach. Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects’ perceived engagement was quantified using a questionnaire. Main results. The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects’ perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p = 0.025 and p < 0.001, respectively); greater during medium and fast compared to slow Tetris speed (p < 0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (r rm = 0.44, p < 0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states. Significance. This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Real-time estimation of EEG-based engagement in different tasks

Objective. Recent trends in brain–computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG). Approach. Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects’ perceived engagement was quantified using a questionnaire. Main results. The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects’ perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p = 0.025 and p < 0.001, respectively); greater during medium and fast compared to slow Tetris speed (p < 0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (r rm = 0.44, p < 0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states. Significance. This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Real-time estimation of EEG-based engagement in different tasks

Objective. Recent trends in brain–computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG). Approach. Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects’ perceived engagement was quantified using a questionnaire. Main results. The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects’ perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p = 0.025 and p < 0.001, respectively); greater during medium and fast compared to slow Tetris speed (p < 0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (r rm = 0.44, p < 0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states. Significance. This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Data_Sheet_1_EEG Biomarkers Related With the Functional State of Stroke Patients.PDF

IntroductionRecent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment.MethodsThirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps.ResultsThe results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P ConclusionThe quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.</p