Objective: The purpose of the present study is to evaluate the relationship between working memory and BCI performance.
Methods: Participants took part in two separate sessions. The first session consisted of three computerized tasks. The List Sorting Working Memory Task was used to measure working memory, the Picture Vocabulary Test was used to measure general intelligence, and the Dimensional Change Card Sort Test was used to measure executive function, specifically cognitive flexibility. The second session consisted of a P300-based BCI copy-spelling task.
Results: The results indicate that both working memory and general intelligence are significant predictors of BCI performance.
Conclusions: This suggests that working memory training could be used to improve performance on a BCI task.
Significance: Working memory training may help to reduce a portion of the individual differences that exist in BCI performance allowing for a wider range of users to successfully operate the BCI system as well as increase the BCI performance of current users.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747807 | PMC |
| http://dx.doi.org/10.1016/j.clinph.2015.10.038 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms.
NPJ Digit Med
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training.
View Article and Find Full Text PDFAspect category sentiment analysis based on pre-trained BiLSTM and syntax-aware graph attention network.
Sci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
View Article and Find Full Text PDFPrioritizing working memory resources depends on prefrontal cortex.
J Neurosci
January 2025
Department of Psychology, New York University
How the prefrontal cortex contributes to working memory remains controversial, as theories differ in their emphasis on its role in storing memories versus controlling their content. To adjudicate between these competing ideas, we tested how perturbations to the human (both sexes) lateral prefrontal cortex impact the storage and control aspects of working memory during a task that requires human subjects to allocate resources to memory items based on their behavioral priority. Our computational model made a strong prediction that disruption of this control process would counterintuitively improve memory for low-priority items.
View Article and Find Full Text PDFThe Extended Mind in Young Children: Cost-Dependent Trade-Off Between External and Internal Memory.
Psychol Sci
January 2025
Department of Psychology, University of Massachusetts Boston.
Most work on working memory development has children remember a set of items as well as they can. However, this approach sidesteps the , the integration of external information with memory. Indeed, adults prefer to use external resources (e.
View Article and Find Full Text PDFMultimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.
JMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!