An Electrocorticographic Brain Interface in an Individual with Tetraplegia

Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals

The effect of voluntary modulation of the sensory-motor rhythm during different mental tasks on H reflex

Objectives: The aim of this study was to explore the possibility of the short-term modulation of the soleus H reflex through self-induced modulation of the sensory-motor rhythm (SMR) as measured by electroencephalography (EEG) at Cz. Methods: Sixteen healthy participants took part in one session of neuromodulation. Motor imagery and mental math were strategies for decreasing SMR, while neurofeedback was used to increase SMR. H reflex of the soleus muscle was elicited by stimulating tibial nerve when SMR reached a pre-defined threshold and was averaged over 5 trials. Results: Neurofeedback and mental math both resulted in the statistically significant increase of H reflex (p = 1.04·10− 6 and p = 5.47·10− 5 respectively) while motor imagery produced the inconsistent direction of H reflex modulation (p = 0.57). The average relative increase of H reflex amplitude was for neurofeedback 19.0 ± 5.4%, mental math 11.1 ± 3.6% and motor imagery 2.6 ± 1.0%. A significant negative correlation existed between SMR amplitude and H reflex for all tasks at Cz and C4. Conclusions: It is possible to achieve a short-term modulation of H reflex through short-term modulation of SMR. Various mental tasks dominantly facilitate H reflex irrespective of direction of SMR modulation. Significance: Improving understanding of the influence of sensory-motor cortex on the monosynaptic reflex through the self-induced modulation of cortical activity

An uncued brain-computer interface using reservoir computing

Brain-Computer Interfaces are an important and promising avenue for possible next-generation assistive devices. In this article, we show how Reservoir Comput- ing – a computationally efficient way of training recurrent neural networks – com- bined with a novel feature selection algorithm based on Common Spatial Patterns can be used to drastically improve performance in an uncued motor imagery based Brain-Computer Interface (BCI). The objective of this BCI is to label each sample of EEG data as either motor imagery class 1 (e.g. left hand), motor imagery class 2 (e.g. right hand) or a rest state (i.e., no motor imagery). When comparing the re- sults of the proposed method with the results from the BCI Competition IV (where this dataset was introduced), it turns out that the proposed method outperforms the winner of the competition

Multiple roles of motor imagery during action observation

Over the last 20 years, the topics of action observation (AO) and motor imagery (MI) have been largely studied in isolation from each other, despite the early integrative account by Jeannerod (1994, 2001). Recent neuroimaging studies demonstrate enhanced cortical activity when AO and MI are performed concurrently (“AO+MI”), compared to either AO or MI performed in isolation. These results indicate the potentially beneficial effects of AO+MI, and they also demonstrate that the underlying neurocognitive processes are partly shared. We separately review the evidence for MI and AO as forms of motor simulation, and present two quantitative literature analyses that indeed indicate rather little overlap between the two bodies of research. We then propose a spectrum of concurrent AO+MI states, from congruent AO+MI where the contents of AO and MI widely overlap, over coordinative AO+MI, where observed and imagined action are different but can be coordinated with each other, to cases of conflicting AO+MI. We believe that an integrative account of AO and MI is theoretically attractive, that it should generate novel experimental approaches, and that it can also stimulate a wide range of applications in sport, occupational therapy, and neurorehabilitation

MEG:hen perustuvan aivo-tietokone -käyttöliittymän kehitys

Brain–computer interfaces (BCI) have recently gained interest both in basic neuroscience and clinical interventions. The majority of noninvasive BCIs measure brain activity with electroencephalography (EEG). However, the real-time signal analysis and decoding of brain activity suffer from low signal-to-noise ratio and poor spatial resolution of EEG. These limitations could be overcome by using magnetoencephalography (MEG) as an alternative measurement modality. The aim of this thesis is to develop an MEG-based BCI for decoding hand motor imagery, which could eventually serve as a therapeutic method for patients recovering from e.g. cerebral stroke. Here, machine learning methods for decoding motor imagery -related brain activity are validated with healthy subjects’ MEG measurements. The first part of the thesis (Study I) involves a comparison of feature extraction methods for classifying left- vs right-hand motor imagery (MI), and MI vs rest. It was found that spatial filtering and further extraction of bandpower features yield better classification accuracy than time–frequency features extracted from parietal gradiometers. Furthermore, prior spatial filtering improved the discrimination capability of time–frequency features. The training data for a BCI is typically collected in the beginning of each measurement session. However, as this can be time-consuming and exhausting for the subject, the training data from other subjects’ measurements could be used as well. In the second part of the thesis (Study II), methods for across-subject classification of MI were compared. The results showed that a classifier based on multi-task learning with a l2,1-norm regularized logistic regression was the best method for across-subject decoding for both MEG and EEG. In Study II, we also compared the decoding results of simultaneously measured EEG and MEG data, and investigated whether the MEG responses to passive hand movements could be used to train a classifier to detect MI. MEG yielded altogether slightly, but not significantly, better results than EEG. Training the classifiers with subject’s own or other subjects’ passive movements did not result in high accuracy, which indicates that passive movements should not be used for calibrating an MI-BCI. The methods presented in this thesis are suitable for a real-time MEG-based BCI. The decoding results can be used as a benchmark when developing other classifiers specifically for motor imagery -related MEG data.Aivo-tietokone -käyttöliittymät (brain–computer interface; BCI) ovat viime aikoina herättäneet kiinnostusta niin neurotieteen perustutkimuksessa kuin kliinisissä interventioissakin. Suurin osa ei-invasiivisista BCI:stä mittaa aivotoimintaa elektroenkefalografialla (EEG). EEG:n matala signaali-kohinasuhde ja huono avaruudellinen resoluutio kuitenkin hankaloittavat reaaliaikais-ta signaalianalyysia ja aivotoiminnan luokittelua. Nämä rajoitteet voidaan kiertää käyttämällä magnetoenkefalografiaa (MEG) vaihtoehtoisena mittausmenetelmänä. Tämän työn tavoitteena on kehittää käden liikkeen kuvittelua luokitteleva, MEG:hen perustuva BCI, jota voidaan myöhemmin käyttää terapeuttisena menetelmänä esimerkiksi aivoinfarktista toipuvien potilaiden kuntoutuk-sessa. Tutkimuksessa validoidaan terveillä koehenkilöillä tehtyjen MEG-mittausten perusteella koneoppimismenetelmiä, joilla luokitellaan liikkeen kuvittelun aiheuttamaa aivotoimintaa. Ensimmäisessä osatyössä (Tutkimus I) vertailtiin piirteenirrotusmenetelmiä, joita käytetään erottamaan toisistaan vasemman ja oikean käden kuvittelu sekä liikkeen kuvittelu ja lepotila. Ha-vaittiin, että avaruudellisesti suodatettujen signaalien taajuuskaistan teho luokittelupiirteenä tuotti parempia luokittelutarkkuuksia kuin parietaalisista gradiometreistä mitatut aika-taajuuspiirteet. Lisäksi edeltävä avaruudellinen suodatus paransi aika-taajuuspiirteiden erottelukykyä luokittelu-tehtävissä.BCI:n opetusdata kerätään yleensä kunkin mittauskerran alussa. Koska tämä voi kuitenkin olla aikaavievää ja uuvuttavaa koehenkilölle, opetusdatana voidaan käyttää myös muilta koehenkilöiltä kerättyjä mittaussignaaleja. Toisessa osatyössä (Tutkimus II) vertailtiin koehenkilöiden väliseen luo-kitteluun soveltuvia menetelmiä. Tulosten perusteella monitehtäväoppimista ja l2,1-regularisoitua logistista regressiota käyttävä luokittelija oli paras menetelmä koehenkilöiden väliseen luokitteluun sekä MEG:llä että EEG:llä. Toisessa osatyössä vertailtiin myös samanaikaisesti mitattujen MEG:n ja EEG:n tuottamia luokit-telutuloksia, sekä tutkittiin voidaanko passiivisten kädenliikkeiden aikaansaamia MEG-vasteita käyttää liikkeen kuvittelua tunnistavien luokittelijoiden opetukseen. MEG tuotti hieman, muttei merkittävästi, parempia tuloksia kuin EEG. Luokittelijoiden opetus koehenkilöiden omilla tai mui-den koehenkilöiden passiiviliikkeillä ei tuottanut hyviä luokittelutarkkuuksia, mikä osoittaa että passiiviliikkeitä ei tulisi käyttää liikkeen kuvittelua tunnistavan BCI:n kalibrointiin. Työssä esitettyjä menetelmiä voidaan käyttää reaaliaikaisessa MEG-BCI:ssä. Luokittelutuloksia voidaan käyttää vertailukohtana kehitettäessä muita liikkeen kuvitteluun liittyvän MEG-datan luokittelijoita

Neural Prosthetic Advancement: identification of circuitry in the Posterior Parietal Cortex

There are limited options for rehabilitation following an established Spinal Cord Injury (SCI) resulting in paralysis. For most of the individuals affected, SCI means a lifetime of confinement to a wheelchair and overall reduced independence. Brain-Computer and Brain-Machine Interface (BCI and BMI) techniques may be of aid when used for assistive purposes. However, these techniques are still far from being implemented in daily rehabilitative practice. Existing literature on the use of BCI and BMI techniques in SCI is limited and focuses on the extraction of motor control signals from the primary motor cortex (M1). However, evidence suggests that in long-term established SCI the functional activation of motor and premotor areas tends to decrease over time. In the present project, we explore the possibility of successful implementation of assistive BCI and BMI systems using posterior parietal areas as extraction sites of motor control activity. Firstly, we will investigate the representation of space in the posterior parietal cortex (PPC) and whether evidence of body-centered reference frames can be found in healthy individuals. We will then proceed to extract information regarding the residual level of motor imagery activity in individuals suffering from long-term and high-level SCI. Our aim is to ascertain whether functional activation of motor and posterior areas is comparable to that of matched controls. Finally, we will present work that was done in collaboration with the Netherlands Organisation for Applied Scientific Research that can offer an example of successful application of a BCI technique for rehabilitation purposes

Brain-machine interfaces for rehabilitation in stroke: A review

BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.This study was funded by the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ13GW0053)andAMORSA(FKZ16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the fortüne-Program of the University of Tübingen (2422-0-0 and 2452-0-0), and the Basque GovernmentScienceProgram(EXOTEK:KK2016/00083). NIL was supported by the Basque Government’s scholarship for predoctoral students