Shoham Jacobsen1, Oded Meiron2, David Yoel Salomon1, Nir Kraizler1, Hagai Factor2, Efraim Jaul3, Elishai Ezra Tsur1,4. 1. 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel. 2. 2Clinical Research Center for Brain SciencesHerzog Medical CenterJerusalem91120Israel. 3. 3Geriatric Skilled Nursing DepartmentHerzog Medical CenterJerusalem91120Israel. 4. 4Neuro-Biomorphic Engineering Laboratory (NBEL)Department of Mathematics and Computer ScienceThe Open University of IsraelRa'anana4353701Israel.
Abstract
Background: EEG-driven research is paramount in cognitive-neuropsychological studies, as it provides a non-invasive window to the underlying neural mechanisms of cognition and behavior. A myriad collection of software and hardware frameworks has been developed to alleviate some of the technical barriers involved in EEG-driven research. Methods: we propose an integrated development environment which encompasses the entire technical "data-collection pipeline" of cognitive-neuropsychological research, including experiment design, data acquisition, data exploration and analysis in a state-of-the-art user interface. Our framework is based on a unique integration between a python-based web framework, time-oriented databases and object-based data schemes. Results: we demonstrated our framework with the recording and analysis of an n-Back task completed by 15 elderly (ages 50 to 80) participants. This case study demonstrates the highly utilized nature of our integrated framework with a challenging target population. Furthermore, our results may provide new insights into the correlation between brain activity and working memory performance in elderly people, who are prone to experience accelerated decline in executive prefrontal cortex functioning. Conclusion: our framework extends the range of EEG-driven experimental methods for assessing cognition available for cognitive-neuroscientists, allowing them to concentrate on the creative part of their work instead of technical aspects.
Background: EEG-driven research is paramount in cognitive-neuropsychological studies, as it provides a non-invasive window to the underlying neural mechanisms of cognition and behavior. A myriad collection of software and hardware frameworks has been developed to alleviate some of the technical barriers involved in EEG-driven research. Methods: we propose an integrated development environment which encompasses the entire technical "data-collection pipeline" of cognitive-neuropsychological research, including experiment design, data acquisition, data exploration and analysis in a state-of-the-art user interface. Our framework is based on a unique integration between a python-based web framework, time-oriented databases and object-based data schemes. Results: we demonstrated our framework with the recording and analysis of an n-Back task completed by 15 elderly (ages 50 to 80) participants. This case study demonstrates the highly utilized nature of our integrated framework with a challenging target population. Furthermore, our results may provide new insights into the correlation between brain activity and working memory performance in elderly people, who are prone to experience accelerated decline in executive prefrontal cortex functioning. Conclusion: our framework extends the range of EEG-driven experimental methods for assessing cognition available for cognitive-neuroscientists, allowing them to concentrate on the creative part of their work instead of technical aspects.
Entities:
Keywords:
Emotiv; Python; working memory in elderly
Authors: Michael P Barham; Gillian M Clark; Melissa J Hayden; Peter G Enticott; Russell Conduit; Jarrad A G Lum Journal: Psychophysiology Date: 2017-05-12 Impact factor: 4.016