Cesar Marquez-Chin

Automatic detection of expressed emotion in Parkinson's Disease

Patients with Parkinsons Disease (PD) frequently exhibit deficits in the production of emotional speech. In this paper, we examine the classification of emotional speech in patients with PD and the classification of PD speech. Participants were recorded speaking short statements with different emotional prosody which were classified with three methods (naïve Bayes, random forests, and support vector machines) using 209 unique auditory features. Feature sets were reduced using simple statistical testing. We achieve accuracies of 65.5% and 73.33% on classifying between the emotions and between PD vs. control, respectively. These results may assist in the future development of automated early detection systems for diagnosing patients with PD.

Real-time two-dimensional asynchronous control of a computer cursor with a single subdural electrode

Abstract Objective To test the feasibility of controlling a computer cursor asynchronously in two dimensions using one subdural electrode. Design Proof of concept study. Setting Acute care hospital in Toronto, Canada. Participant A 68-year-old woman with a subdural electrode implanted for the treatment of essential tremor (ET) using direct brain stimulation of the primary motor cortex (MI). Interventions Power changes in the electrocorticography signals were used to implement a “brain switch”. To activate the switch the subject had to decrease the power in the 7–13 Hz frequency range using motor imagery of the left hand. The brain switch was connected to a system for asynchronous control of movement in two dimensions. Each time the user reduced the amplitude in the 7–13 Hz frequency band below an experimentally defined threshold the direction of cursor changed randomly. The new direction was always different from those previously rejected ensuring the convergence of the system on the desired direction. Outcome measures Training time, time and number of switch activations required to reach specific targets, information transfer rate. Results The user was able to control the cursor to specific targets on the screen after only 15 minutes of training. Each target was reached in 51.7 ± 40.2 seconds (mean ± SD) and after 9.4 ± 6.8 switch activations. Information transfer rate of the system was estimated to be 0.11 bit/second. Conclusion A novel brain–machine interface for asynchronous two-dimensional control using one subdural electrode was developed.

EEG-Triggered Functional Electrical Stimulation Therapy for Restoring Upper Limb Function in Chronic Stroke with Severe Hemiplegia

We report the therapeutic effects of integrating brain-computer interfacing technology and functional electrical stimulation therapy to restore upper limb reaching movements in a 64-year-old man with severe left hemiplegia following a hemorrhagic stroke he sustained six years prior to this study. He completed 40 90-minute sessions of functional electrical stimulation therapy using a custom-made neuroprosthesis that facilitated 5 different reaching movements. During each session, the participant attempted to reach with his paralyzed arm repeatedly. Stimulation for each of the movement phases (e.g., extending and retrieving the arm) was triggered when the power in the 18 Hz–28 Hz range (beta frequency range) of the participants EEG activity, recorded with a single electrode, decreased below a predefined threshold. The function of the participants arm showed a clinically significant improvement in the Fugl-Meyer Assessment Upper Extremity (FMA-UE) subscore (6 points) as well as moderate improvement in Functional Independence Measure Self-Care subscore (7 points). The changes in arms function suggest that the combination of BCI technology and functional electrical stimulation therapy may restore voluntary motor function in individuals with chronic hemiplegia which results in severe upper limb deficit (FMA-UE ≤ 15), a population that does not benefit from current best-practice rehabilitation interventions.

BCI-Triggered functional electrical stimulation therapy for upper limb

We present here the integration of brain-computer interfacing (BCI) technology with functional electrical stimulation therapy to restore voluntary function. The system was tested with a single man with chronic (6 years) severe left hemiplegia resulting from a stroke. The BCI, implemented as a simple “brain-switch” activated by power decreases in the 18 Hz – 28 Hz frequency range of the participant’s electroencephalograpic signals, triggered a neuroprosthesis designed to facilitate forward reaching, reaching to the mouth, and lateral reaching movements. After 40 90-minute sessions in which the participant attempted the reaching tasks repeatedly, with the movements assisted by the BCI-triggered neuroprosthesis, the participant’s arm function showed a clinically significant six point increase in the Fugl-Meyer Asessment Upper Extermity Sub-Score. These initial results suggest that the combined use of BCI and functional electrical stimulation therapy may restore voluntary reaching function in individuals with chronic severe hemiplegia for whom the rehabilitation alternatives are very limited.

EEG-Controlled Functional Electrical Stimulation Therapy With Automated Grasp Selection: A Proof-of-Concept Study

Background: Functional electrical stimulation therapy (FEST) is a promising intervention for the restoration of upper extremity function after cervical spinal cord injury (SCI). Objectives: This study describes and evaluates a novel FEST system designed to incorporate voluntary movement attempts and massed practice of functional grasp through the use of brain-computer interface (BCI) and computer vision (CV) modules. Methods: An EEG-based BCI relying on a single electrode was used to detect movement initiation attempts. A CV system identified the target object and selected the appropriate grasp type. The required grasp type and trigger command were sent to an FES stimulator, which produced one of four multichannel muscle stimulation patterns (precision, lateral, palmar, or lumbrical grasp). The system was evaluated with five neurologically intact participants and one participant with complete cervical SCI. Results: An integrated BCI-CV-FES system was demonstrated. The overall classification accuracy of the CV module was 90.8%, when selecting out of a set of eight objects. The average latency for the BCI module to trigger the movement across all participants was 5.9 ± 1.5 seconds. For the participant with SCI alone, the CV accuracy was 87.5% and the BCI latency was 5.3 ± 9.4 seconds. Conclusion: BCI and CV methods can be integrated into an FEST system without the need for costly resources or lengthy setup times. The result is a clinically relevant system designed to promote voluntary movement attempts and more repetitions of varied functional grasps during FEST.

Brain–computer interfaces for neurorehabilitation: enhancing functional electrical stimulation

Abstract This chapter presents the integration of functional electrical stimulation therapy and brain–computer interfaces to restore voluntary motor function after paralysis resulting from stroke and spinal cord injury. Functional electrical stimulation therapy is a short-term intervention in which patients attempt a series of functional tasks while a train of electrical pulses, triggered by a therapist, produces contractions of the muscles required to produce the intended movement. It is believed that the simultaneous presence of a motor command, produced by the attempted motion, and the corresponding sensory information, resulting from the artificially produced movement, promote changes in the nervous system that result in improved motor function after the therapy. Brain–computer interfacing technology offers a new opportunity to use indicators of the intention to move in the electroencephalographic activity to trigger the electrical stimulation. Early results suggest that this combination of technologies is effective for motor rehabilitation even in the most severe cases of paralysis.

Reconstruction of reaching movement trajectories using electrocorticographic signals in humans

In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.

Welcome to the 7th National Spinal Cord Injury Conference! Celebrating our history

Welcome to the 7 National Spinal Cord Injury Conference! Celebrating our history Cesar Marquez-Chin & Jennifer Mokry To cite this article: Cesar Marquez-Chin & Jennifer Mokry (2017) Welcome to the 7 National Spinal Cord Injury Conference! Celebrating our history, The Journal of Spinal Cord Medicine, 40:6, 631-640, DOI: 10.1080/10790268.2017.1370434 To link to this article: http://dx.doi.org/10.1080/10790268.2017.1370434

Prediction of specific hand movements using electroencephalographic signals

Objective: To identify specific hand movements from electroencephalographic activity. Design: Proof of concept study. Setting: Rehabilitation hospital in Toronto, Canada. Participants: Fifteen healthy individuals with no neurological conditions. Intervention: Each individual performed six different hand movements, including four grasps commonly targeted during rehabilitation. All of them used their dominant hand and four of them repeated the experiment with their non-dominant hand. EEG was acquired from 8 different locations (C1, C2, C3, C4, CZ, F3, F4 and Fz). Time-frequency distributions (spectrogram) of the pre-movement EEG activity for each electrode were generated and each of the time-resolved spectral components (1 Hz to 50 Hz) was correlated with a hyperbolic tangent function to detect power decreases. The spectral components and time ranges with the largest correlation values were identified using a threshold. The resulting features were then used to implement a distance-based classifier. Outcome measures: Accuracy of classification. Results: A minimum of three different dominant hand movements were classified correctly with average accuracies between 65–75% across all 15 participants. Average accuracies between 67–85% for the same three movements were achieved across four of the 15 participants who were tested with their non-dominant hand. Conclusion: The results suggest that it may be possible to predict specific hand movements from a small number of electroencephalographic electrodes. Further studies including members of the spinal cord injury community are necessary to verify the suitability of the proposed process.

Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study

Context: Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Findings: Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as ‘fast-spiking’, ‘bursting’ or ‘regular-spiking’ according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. Conclusion/Clinical Relevance: The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.