Marisol Rodriguez-Ugarte

Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power

BackgroundTranscranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation. Considering that the cerebellum and motor cortex exert influence on the motor network, their stimulation could enhance motor functions, such as motor imagery, and be utilized for brain-computer interfaces (BCIs) during motor neurorehabilitation.MethodsA new tDCS montage that influences cerebellum and either right-hand or feet motor area is proposed and validated with a simulation of electric field. The effect of current density (0, 0.02, 0.04 or 0.06 mA/cm2) on electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on 5 healthy volunteers for different stimulation modalities: 1) 10-minutes anodal tDCS before EEG acquisition over right-hand or 2) feet motor cortical area, and 3) 4-seconds anodal tDCS during EEG acquisition either on right-hand or feet cortical areas before each time right-hand or feet motor imagery is performed. For each subject and tDCS modality, analysis of variance and Tukey-Kramer multiple comparisons tests (p <0.001) are used to detect significant differences between classification accuracies that are obtained with different current densities. For tDCS modalities that improved accuracy, t-tests (p <0.05) are used to compare μ and β band power when a specific current density is provided against the case of supplying no stimulation.ResultsThe proposed montage improved the classification of right-hand motor imagery for 4 out of 5 subjects when the highest current was applied for 10 minutes over the right-hand motor area. Although EEG band power changes could not be related directly to classification improvement, tDCS appears to affect variably different motor areas on μ and/or β band.ConclusionsThe proposed montage seems capable of enhancing right-hand motor imagery detection when the right-hand motor area is stimulated. Future research should be focused on applying higher currents over the feet motor cortex, which is deeper in the brain compared to the hand motor cortex, since it may allow observation of effects due to tDCS. Also, strategies for improving analysis of EEG respect to accuracy changes should be implemented.

Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent

The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and pseudo-online (in a way suitable for real-time applications), with a preference for the latter case. A process for selecting the best BCI model was described in detail. Results for the pseudo-online processing with the best BCI model of each subject were on average 76.7% of true positive rate, 4.94 false positives per minute and 55.1% of accuracy. The personalized BCI model approach was also found to be significantly advantageous when compared to the typical approach of using a fixed feature extraction algorithm and electrode configuration. The resulting approach could be used to more robustly interface with lower limb exoskeletons in the context of the rehabilitation of stroke patients.

Detection of intention of pedaling start cycle through EEG signals

Recovery from cerebrovascular accident (CVA) is a growing research topic. Exoskeletons are being used for this purpose in combination with a volitional control algorithm. This work studied the intention of pedaling initiation movement, based on previous work, with different types of electrode configuration and different processing time windows. The main characteristic is to find alterations in the mu and beta frequency bands where ERD/ERS is produced. The results show that for the majority of the subjects this event is well detected with 8 or 9 electrodes and using time before and after the movement onset.Recovery from cerebrovascular accident (CVA) is a growing research topic. Exoskeletons are being used for this purpose in combination with a volitional control algorithm. This work studied the intention of pedaling initiation movement, based on previous work, with different types of electrode configuration and different processing time windows. The main characteristic is to find alterations in the mu and beta frequency bands where ERD/ERS is produced. The results show that for the majority of the subjects this event is well detected with 8 or 9 electrodes and using time before and after the movement onset.

Pseudo-Online Detection of Intention of Pedaling Start Cycle Through EEG Signals

This work studied different electrode configurations and processing windows for detecting the intention of pedaling initiation. Furthermore, data were pseudo-online analyzed. The main goal was to find alterations in the mu and beta frequency bands where event-related synchronization and desynchronization (ERS/ERD) is produced. The results show an improvement using time before and after the movement onset rather than until the movement onset.

Study of Algorithms Classifiers for an Offline BMI Based on Motor Imagery of Pedaling

The paper compares different signal processing algorithms and classifiers to evaluate the accuracy of a BMI based on lower-limb motor imagery. The methods were based on the analysis of the peaks of the different processing epochs for the alpha, beta and gamma EEG bands through the Marginal Hilbert Spectrum, Power Spectral Density and Fourier harmonic components. Data were classified and analyzed by three classifiers: Support Vector Machine, Self-Organizing Maps and Linear Discriminator analysis. Results show accuracy is dependent on the subject, but there is not dependency between the subjects and the methods, and classifiers. Best accuracy results were achieved by using the value of the peak of the Hilbert Marginal Spectrum, obtaining the analytical signal with the Stockwell transform. Regarding the classifiers SOM presented lower accuracy values than SVM and LDA.

Improving real-time lower limb motor imagery detection using tDCS and an exoskeleton

The aim of this work was to test if a novel transcranial direct current stimulation (tDCS) montage boosts the accuracy of lower limb motor imagery (MI) detection by using a real-time brain-machine interface (BMI) based on electroencephalographic (EEG) signals. The tDCS montage designed was composed of two anodes and one cathode: one anode over the right cerebrocerebellum, the other over the motor cortex in Cz, and the cathode over FC2 (using the International 10–10 system). The BMI was designed to detect two MI states: relax and gait MI; and was based on finding the power at the frequency which attained the maximum power difference between the two mental states at each selected EEG electrode. Two different single-blind experiments were conducted, E1 and a pilot test E2. E1 was based on visual cues and feedback and E2 was based on auditory cues and a lower limb exoskeleton as feedback. Twelve subjects participated in E1, while four did so in E2. For both experiments, subjects were separated into two equally-sized groups: sham and active tDCS. The active tDCS group achieved 12.6 and 8.2% higher detection accuracy than the sham group in E1 and E2, respectively, reaching 65 and 81.6% mean detection accuracy in each experiment. The limited results suggest that the exoskeleton (E2) enhanced the detection of the MI tasks with respect to the visual feedback (E1), increasing the accuracy obtained in 16.7 and 21.2% for the active tDCS and sham groups, respectively. Thus, the small pilot study E2 indicates that using an exoskeleton in real-time has the potential of improving the rehabilitation process of cerebrovascular accident (CVA) patients, but larger studies are needed in order to further confirm this claim.

Classification of Gait Motor Imagery While Standing Based on Electroencephalographic Bandpower

Brain-computer interfaces (BCIs) translate brain signals into commands for a device. BCIs are a complementary option in therapy during gait rehabilitation. This paper presents a strategy based on electroencephalographic (EEG) bandpower for detecting gait motor imagery (MI) while being standing. In particular, \(\mu \) (8–13 Hz) and 20–35 Hz bands were used. Preliminary results show that two out of three users could achieve an accuracy above 70% of correct classifications. The proposed strategy could be used in a MI-based BCI to enhance brain activity associated to the gait process.

Using EEG Signals to Detect Different Surfaces While Walking

Brain-Computer Interfaces are one of the most interesting ways to work in rehabilitation and assistance programs to people who have problems in their lower limb to march. This paper presents evidence by means of statistical analysis sets that there are specific frequencies ranges on EEG signals while walking on four different surfaces: hard floor, soft floor, ramp and stairs, finding proportional differences in predictions between each pair of tasks for every user through the employ of Matlab classifiers. In that way, our results are statistical sets of successful percentages in classification of signals between two tasks. We worked with five different volunteers and we found an average of 76.5% of success in predictions between soft floor and stairs surfaces. Lower results, around 60%, were obtained when differentiating between hard floor/stairs and ramp/stairs. We can notice that magnitude of these percentages fits with a common sense about real physical differences between four kinds of surfaces. This study means a starting point to go deeper in signal morphology analyzing the specific mathematical characteristics of EEG signals while walking on those surfaces and other ones.

Effect on the classification of motor imagery in EEG after applying anodal tDCS with a 4×1 ring montage over the motor cortex

Transcranial direct stimulation (tDCS) is a technique for modulating brain excitability that has potential to be used in motor neurorehabilitation by enhancing motor activity, such as motor imagery (MI). tDCS effects depend on different factors, like current density and the position of the stimulating electrodes. This study presents preliminary results of the evaluation of the effect of current density on MI performance by measuring right-hand/feet MI accuracy of classification from electroencephalographic (EEG) measurements after anodal tDCS is applied with a 4×1 ring montage over the right-hand or feet motor cortex. Results suggest that there might be an enhancement of feet MI when tDCS is applied over the right-hand motor cortex, but further evaluation is required. If results are confirmed with a larger sample, the montage could be used to optimize feet MI performance and improve the outcome of MI-based brain-computer interfaces, which are used during motor neurorehabilitation.

Evaluating Cognitive Mechanisms During Walking from EEG Signals

In this work, three different cognitive mechanisms were analyzed during gait. First, a real-time index of attention level was obtained in real-time. The detection of starting and stopping indices was also evaluated. Finally, the detection of obstacle appearance allows increasing safety during experiments providing a stop command when necessary. The results obtained indicate that it is possible to generate commands related to the patient’s mental state. These commands allow users to be more implicated in their therapies and thus improve their performance.