Unimanual versus bimanual motor imagery classifiers for assistive and rehabilitative brain computer interfaces

Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this study we present a classifier which discriminates between uni-and bimanual MI. Ten able bodied participants took part in cue based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32 channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R and B--L hands were created, with features based on wide band Common Spatial Patterns (CSP) 8-30 Hz, and band specifics Common Spatial Patterns (CSPb). Event related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for L-R task (73±9% and 75±10% respectively) and for L-B task (73±11% and 70±9% respectively). However, for R-B task (67±3% and 72±6% respectively) it was significantly higher for CSPb (p=0.0351). Six participants whose L-R classification accuracy exceeded 70% were included in an on-line task a week later, using the unmodified offline CSPb classifier, achieving 69±3% and 66±3% accuracy for the L-R and R-B tasks respectively. Combined uni and bimanual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits

EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation

The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed. However, there is still a need to create a more reliable BCI that could be used to control several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding different movements from the same limb, high accuracy and reliability are some of the main difficulties when using conventional EEG-based BCIs and the challenges we tackled in this thesis. In this PhD thesis, we investigated that the classification of several functional hand reaching movements from the same limb using EEG is possible with acceptable accuracy. Moreover, we investigated how the recalibration could affect the classification results. For this reason, we tested the recalibration in each multi-class decoding for within session, recalibrated between-sessions, and between sessions. It was shown the great influence of recalibrating the generated classifier with data from the current session to improve stability and reliability of the decoding. Moreover, we used a multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the decoding accuracy based on features and compared it to our previous study using CSP. Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges as a way to influence brain plasticity or controlling external devices. However, neural oscillations have shown to synchronize activity according to motor and cognitive functions. For this reason, the existence of cross-frequency interactions produces oscillations with different frequencies in neural networks. In this PhD, we investigated for the first time the existence of cross-frequency coupling during rest and movement using ECoG in chronic stroke patients. We found that there is an exaggerated phase-amplitude coupling between the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also reported in another neurological disorder affecting motor function (Parkinson and dystonia) but, to date, it has not been investigated in stroke patients. This finding might change the future design of assistive or therapeuthic BCI systems for motor restoration in stroke patients

Developing robust movement decoders for local field potentials

textBrain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms.Electrical and Computer Engineerin

EEG Classification of Different Imaginary Movements within the Same Limb

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises