Skip to main content

Effective Nov. 1, 2023, clients and families, visitors, vendors and staff are required to wear a mask while moving throughout the hospital, including while in elevators, in spaces where clients receive care or participate in research. A medical grade mask will be available upon entry.


2016 Student Defenses

Samantha D'Souza, MSc

CP Discovery lab

 "Over the past two years, the experiences I have gained at the BRI have truly been invaluable. I am grateful for the endless encouragement and support I have received, which has not only helped to increase my skill set, but has also fostered new academic opportunities."

Investigating Sensory Plasticity in Children with Hemiplegic Cerebral palsy Following Constraint-Induced Movement Therapy

Children with hemiplegic cerebral palsy (HCP) experience upper limb sensory processing and motor deficits. While constraint-induced movement therapy (CIMT) is effective in improving motor hand function in HCP, its impact on sensory function remains under-investigated. The present study evaluates the effectiveness of CIMT on sensory function in children with HCP using neuroimaging and clinical diagnostic tools. Ten children with HCP attended a three-week CIMT intervention with the integration of a sensory component to optimize potential sensory change. Both magnetoencephalography (MEG) and clinical sensory/motor assessments were completed at: baseline (one week prior to CIMT), one and six months post-baseline. Clinical sensory and MEG measures were compared between all three time points. CIMT did not result in significant changes in clinical sensory modalities or MEG somatosensory processing of the affected hand. This suggests that sensation may be less remedial to CIMT intervention than enhanced upper limb motor function and hand usage. Other potential interpretations include the  contribution of lower synaptogenesis ability for a subcortical axonal injury versus cortical lesion injury and high baseline sensory function. This is the first study to investigate the effect of CIMT on sensory function utilizing clinical sensory measures and neural processing.

Jessica Tomasi, MHSc


"The Bloorview Research Institute is a uniquely collaborative and inspiring place to work and study. Both the academic support and sense of community within our lab, and the Research Institute as a whole, enriched my graduate student experience and made my time at Holland Bloorview two years to remember. Thank you!"

Development and evaluation of a Sensor System to monitor the stance-phase control function of the Automatic Stance-Phase Lock (ASPL) mechanism

The Automatic Stance-Phase Lock is the novel stance-phase control mechanism employed by the All-Terrain Knee. Gait analysis tools are often limited to controlled environments and cannot directly monitor the ASPL. The objective of this project was to design and test a sensor system to measure ASPL function and to begin to explore the effects of relevant alignment, terrain, and mobility conditions on its performance. 

The results of the study indicate that the developed system is sensitive to knee lock position changes, knee extension and flexion, and gait events. Data collected by the system confirms the fundamental relationships between applied moments and knee lock engagement which defines ASPL stance-phase control. Measurable differences in ASPL function allude to its responsiveness to variable gait conditions.

The developed system has the proven potential for use in larger biomechanical and clinical studies to inform All-Terrain Knee design iterations and optimise patient-specific prosthetic alignment and set-up.

Sarah Sarabadani, MASc

Autism Research Centre

"Having the opportunity of working in the warm and supportive environment of BRI provided me with lots of valuable professional outcomes. Conducting study with direct interaction with children with autism and feeling that you have an impact on their lives was a great and exciting part of my work."

Physiological Detection of Emotional States in Children with Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) is associated with difficulties in emotion processing including attributing emotional states to others and processing of one’s own emotional experiences. These difficulties are linked to core social impairments and increased severity of psychiatric co-morbidities such as depression. The nature of these difficulties has remained largely unknown. This is partially due to limitations in obtaining reliable self report of emotional experiences in this population.

Emotion detection using physiological signals is a promising direction in addressing this limitation. Physiological signals can provide a language free method for understanding emotional states in ASD. The use of this approach has not been studied in ASD.

To this end we develop a physiological approach to detection of emotion in children with ASD. We showed that emotional states can be classified with accuracies>80% in a sample of children with ASD which affirms the feasibility of discriminating affective states in this population.

Zahra Emami, MASc

prism lab

"It has been a wonderful past couple of years getting to know the talented and passionate people that make up BRI. If the BRI environment was any more enjoyable than it was, I would have been distracted from my own research!"

Addressing the Effects of Distractors in a Brain-Computer Interface

A brain-computer interface (BCI) is a technology that allows its user to operate a device or application by means of cognitive activity. Because they do not require any motor input, BCIs have significant applications for individuals with severely impaired motor control. Therefore, the ultimate setting for the use of BCIs are real-world environments such as clinics and homes. However, the effects of certain variables present in the real-world, which may not be present in the lab where BCI research is normally conducted, may impede the ease and successful use of BCIs by the end-user. One environmental factor whose influence on BCIs has yet to be systematically examined is a distractor. My thesis investigated the effect of transient, visual task-irrelevant distractors on a motor imagery BCI using electroencephalography (EEG) to measure brain signals. While distractors had an adverse effect on motor imagery-related brain activation patterns, and on a physiological measure of cognitive load, the BCI system was relatively robust to distractor effects on performance. This thesis demonstrated that such a BCI system may be promising for eventual real-world use, although efforts at reducing distractor-related increases in cognitive load on BCI users need to be made to improve the ease-of-use of the technology.

Nicole Proulx, MASc

prism lab

"The BRI was a positive and supportive environment which continuously motivated me to learn more and pursue research which would improve quality of life."

Detection and Online Classification of the Near-Infrared Spectroscopy Fast Optical Signal for Brain-Computer Interfaces

Near-infrared spectroscopy (NIRS) can detect a fast optical signal (FOS), corresponding to optical property changes in neuronal tissue during neuronal activation. The FOS has high temporal and spatial resolution, but has a low signal-to-noise ratio. The FOS has yet to be automatically classified, hence its value as a BCI control signal remains unknown. During offline and online sessions, participants performed a visual oddball task. In offline sessions, the FOS was validated with electroencephalography (EEG) measurements of event-related potential (ERPs). Spectral relationships between FOS and ERP oddball responses were found in upper delta and theta bands using coherence and Granger causality metrics. A temporal FOS-ERP correlation was also found 200 ms after oddball presentation. Offline and online FOS classification of oddballs versus frequent responses was achieved with average balanced accuracies of 62 ± 5% and 63 ± 6%, respectively. FOS classification results were above chance but did not reach the threshold (>70%) for effective BCI communication.

Elias Abou Zied, PhD

prism lab

"I enjoyed a motivating environment, where research is put into practice to enable kids with disabilities."

A Ternary Brain-computer Interface based on Single-trial Readiness Potentials of Self-initiated Fine Movements

This thesis investigates the feasibility of a ternary brain-computer interface (BCI) using the single-trial readiness potential (RP) of self-initiated fine movements. The BCI classified among an idle state and fine motor movements in the left and right hands. Three studies were conducted, each investigating a step towards this ternary BCI. The first study presented a novel technique of feature fusion from an optimal number of electrodes, for the prediction of a self-initiated fine movement from an idle state in single-trials. This technique achieved significantly lower classification error than the best single classifier and some conventional classifier fusion methods. The first study also confirmed the detection of the self-initiated fine movements on a single-trial basis, at above-chance levels, starting 396 ms before their motoric realization. The highest detection accuracy was 82.21% at 250 ms before movement. The second study formulated a novel pipeline of spatio-temporal filtering (PSTF) feature extraction method for laterality classification of self-initiated fine movements. The PSTF achieved significantly higher average classification accuracy (74.99%) than two conventional methods. This second study achieved a significant improvement in single-trial fine movement laterality prediction from RP features alone. The final study investigated a ternary BCI based on a single-trial RP of self-initiated fine movements. The study extended the PSTF for feature extraction in the ternary case, and proposed a diversified classification algorithm for multiclass binary decomposition. The proposed approach achieved significantly higher average classification accuracy (96.24% for idle class; 77.79% for left class; 72.37% for right class) than the conventional multiclass classifier and popular binary decomposition methods. This thesis demonstrates the potential of a ternary BCI based exclusively on RP features. It supports further study of RP-based BCI for intuitive asynchronous control of the environment, such as in augmentative communication or wheelchair navigation.