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Author (aut): Taboada, Maite, Author (aut): Trnavac, Radoslava, Author (aut): Goddard, Cliff
Date created: 2017-04-01
Author (aut): Taylor, Audrey K., Author (aut): Perez, Diane S., Author (aut): Zhang, Xin, Author (aut): Pilapil, Brandy K., Author (aut): Engelhard, Mark H., Author (aut): Gates, Byron D., Author (aut): Rider, David A.
Date created: 2017-09-27
Author (aut): Zhang, Cheng, Author (aut): Zhou, James H.-W., Author (aut): Sameoto, Dan, Author (aut): Zhang, Xin, Author (aut): Li, Yasong, Author (aut): Ng, Him Wai, Author (aut): Menon, Carlo, Author (aut): Gates, Byron D.
Date created: 2012-08-10
The full text of this paper will be available in Mar 2022 due to the embargo policies of Journal of Hazardous Materials. Contact summit@sfu.ca to enquire if the full text of the accepted manuscript can be made available to you.
Author (aut): Belhaj Abdallah, Bouchra, Author (aut): Zhang, Xin, Author (aut): Andreu, Irene, Author (aut): Gates, Byron D., Author (aut): El Mokni, Ridha, Author (aut): Rubino, Stefano, Author (aut): Landoulsi, Ahmed, Author (aut): Chatti, Abdelwaheb
Date created: 2019-11-08
Stroke is one of the leading causes of permanent disability in adults. The literature suggests that rehabilitation is key to early motor recovery. However, conventional therapy is labor and cost intensive. Robotic and functional electrical stimulation (FES) devices can provide a high dose of repetitions and as such may provide an alternative, or an adjunct, to conventional rehabilitation therapy. Brain-computer interfaces (BCI) could augment neuroplasticity by introducing mental training. However, mental training alone is not enough; but combining mental with physical training could boost outcomes. In the current case study, a portable rehabilitative platform and goal-oriented supporting training protocols were introduced and tested with a chronic stroke participant. A novel training method was introduced with the proposed rehabilitative platform. A 37-year old individual with chronic stroke participated in 6-weeks of training (18 sessions in total, 3 sessions a week, and 1 h per session). In this case study, we show that an individual with chronic stroke can tolerate a 6-week training bout with our system and protocol. The participant was actively engaged throughout the training. Changes in the Wolf Motor Function Test (WMFT) suggest that the training positively affected arm motor function (12% improvement in WMFT score).
Author (aut): Zhang, Xin, Author (aut): Elnady, Ahmed M., Author (aut): Randhawa, Bubblepreet K., Author (aut): Boyd, Lara A., Author (aut): Menon, Carlo
Date created: 2018-04-03
Author (aut): Trnavac, Radoslava, Author (aut): Das, Debopam, Author (aut): Taboada, Maite
Date created: 2016
Author (aut): Benamara, Farah, Author (aut): Taboada, Maite, Author (aut): Mathieu, Yannick
Date created: 2017
Author (aut): Goddard, Cliff, Author (aut): Taboada, Maite, Author (aut): Trnavac, Radoslava
Date created: 2017
Author (aut): Zhang, Xin, Author (aut): Park, Hyeong-Ho, Author (aut): Choi, Yong-June, Author (aut): Park, Hyung-Ho , Author (aut): Hill, Ross
Date created: 2011
The full text of this paper will be available in July, 2021 due to the embargo policies of Advanced Optical Materials for works funded by Natural Sciences and Engineering Research Council of Canada (NSERC). Contact summit@sfu.ca to enquire if the full text of the accepted manuscript can be made available to you.
Author (aut): Zhang, Xin, Author (aut): Ali, Rana Faryad , Author (aut): Boyer, John‐Christopher , Author (aut): Branda, Neil R., Author (aut): Gates, Byron D.
Date created: 2020-07-26
The data for this project is a subset of comments from the SFU Opinion and Comments Corpus (SOCC). This subset, the Constructive Comments Corpus (C3) consists of 12, 000 comments annotated by crowdworkers for constructiveness and its characteristics. Citation: Kolhatkar, V., N. Thain, J. Sorensen, L. Dixon and M. Taboada (2020) C3: The Constructive Comments Corpus. Jigsaw and Simon Fraser University. [Data] License: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
For more information about this work, please see: Kolhatkar, V., N. Thain, J. Sorensen, L. Dixon and M. Taboada (to appear) Classifying Constructive Comments. Journal article under review. http://www.sfu.ca/discourse-lab/.
Other related materials: Kolhatkar, V., H. Wu, L. Cavasso, E. Francis, K. Shukla and M. Taboada The SFU Opinion and Comments Corpus: A corpus for the analysis of online news comments. Corpus Pragmatics. https://doi.org/10.1007/s41701-019-00065-w
To access this data, please contact mtaboada@sfu.ca.
For more information about this work, please see: Kolhatkar, V., N. Thain, J. Sorensen, L. Dixon and M. Taboada (to appear) Classifying Constructive Comments. Journal article under review. http://www.sfu.ca/discourse-lab/.
Other related materials: Kolhatkar, V., H. Wu, L. Cavasso, E. Francis, K. Shukla and M. Taboada The SFU Opinion and Comments Corpus: A corpus for the analysis of online news comments. Corpus Pragmatics. https://doi.org/10.1007/s41701-019-00065-w
To access this data, please contact mtaboada@sfu.ca.
Author (aut): Kolhatkar, Varada, Author (aut): Thain, Nithum, Author (aut): Sorensen, Jeffrey, Author (aut): Dixon, Lucas, Author (aut): Taboada, Maite
Date created: 2020-04-01
The SFU Opinion and Comments Corpus (SOCC) is a corpus for the analysis of online news comments. Our corpus contains comments and the articles from which the comments originated. The articles are all opinion articles, not hard news articles. The corpus is larger than any other currently available comments corpora, and has been collected with attention to preserving reply structures and other metadata. In addition to the raw corpus, we also present annotations for four different phenomena: constructiveness, toxicity, negation and its scope, and appraisal. The data is divided into two main parts: raw data and annotated data. The raw data contains three CSVs: gnm_artcles.csv, gnm_comments.csv, and gnm_comment_threads.csv. The annotated data contains annotations for constructiveness, negation, and appraisal. The details of our different corpora and how to use them are on the following GitHub page. https://github.com/sfu-discourse-lab/SOCC/blob/master/README.md. To access this data, please contact mtaboada@sfu.ca.
Author (aut): Kolhatkar, Varada, Author (aut): Wu, Hanhan, Author (aut): Cavasso, Luca, Author (aut): Francis, Emilie, Author (aut): Shukla, Kavan, Author (aut): Taboada, Maite, Author (aut): Saleem, Mehvish
Date created: 2018-01-18
Electroencephalography (EEG) has recently been considered for use in rehabilitation of people with motor deficits. EEG data from the motor imagery of different body movements have been used, for instance, as an EEG-based control method to send commands to rehabilitation devices that assist people to perform a variety of different motor tasks. However, it is both time and effort consuming to go through data collection and model training for every rehabilitation task. In this paper, we investigate the possibility of using an EEG model from one type of motor imagery (e.g.: elbow extension and flexion) to classify EEG from other types of motor imagery activities (e.g.: open a drawer). In order to study the problem, we focused on the elbow joint. Specifically, nine kinesthetic motor imagery tasks involving the elbow were investigated in twelve healthy individuals who participated in the study. While results reported that models from goal-oriented motor imagery tasks had higher accuracy than models from the simple joint tasks in intra-task testing (e.g., model from elbow extension and flexion task was tested on EEG data collected from elbow extension and flexion task), models from simple joint tasks had higher accuracies than the others in inter-task testing (e.g., model from elbow extension and flexion task tested on EEG data collected from drawer opening task). Simple single joint motor imagery tasks could, therefore, be considered for training models to potentially reduce the number of repetitive data acquisitions and model training in rehabilitation applications.
Author (aut): Zhang, Xin, Author (aut): Yong, Xinyi, Author (aut): Menon, Carlo
Date created: 2017-11-29
Author (aut): Paul, Michael T.Y., Author (aut): Yee, Brenden B., Author (aut): Zhang, Xin, Author (aut): Alford, Eiji H., Author (aut): Pilapil, Brandy K., Author (aut): Gates, Byron D.
Date created: 2019-01-01
Fulltext of the document is not available until March 2025 due to the journal embargo policies of the American Chemical Society. If you need fulltext access please email summit@sfu.ca.
Author (aut): Rea, Alex, Author (aut): Zhang, Xin, Author (aut): Mobrhan-Shafiee, Nazanin, Author (aut): Wang, Michael C.P., Author (aut): Proulx, Howard, Author (aut): Gates, Byron
Date created: 2024-03-26