Understanding the interplay between speech and gesture is crucial for linguistic and cognitive research. The current prototype, available on GitHub, aims to automate the analysis of temporal alignment between spoken demonstrative pronouns and pointing gestures in video recordings. By integrating computer vision (via Google’s MediaPipe) and speech recognition (using language-specific Vosk models) using Python, the workflow provides enriched video annotations and alignment data, offering valuable insights into deictic communication.
Reducing the impedance in electroencephalography (EEG) is crucial for capturing high-quality brain activity signals. This process involves ensuring that electrodes make optimal contact with the skin without harming the participant. Below are a few tips to achieve this using a blunt needle, electrolyte gel and gentle wiggling.
Researchers often make participants jump through hoops. Due to our personal blind spots, it seems easier to realise the full extent of these acrobatics when we consider the work of other researchers. In linguistic research, the acrobatics are often spurred by unnatural grammatical constructions.
Say, you need to set up a makeshift EEG lab in an office? Easy-peasy---only, try to move the hardware as little as possible, especially laptops with dongles sticking out. The rest is a trail of snapshots devoid of captions, a sink, a shower room and other paraphernalia, as this is only an ancillary, temporary, extraordinary little lab, and all those staples are within reach in our mainstream lab (see Ledwidge et al., 2018; Luck, 2014).
Electroencephalography (EEG) has become a cornerstone for understanding the intricate workings of the human brain in the field of neuroscience. However, EEG software and hardware come with their own set of constraints, particularly in the management of markers, also known as triggers. This article aims to shed light on these limitations and future prospects of marker management in EEG studies, while also introducing R functions that can help deal with vmrk files from BrainVision.
Electroencephalographic (EEG) signals are often contaminated by muscle artifacts such as blinks, jaw clenching and (of course) yawns, which generate electrical activity that can obscure the brain signals of interest. These artifacts typically manifest as large, abrupt changes in the EEG signal, complicating data interpretation and analysis. To mitigate these issues, participants can be instructed during the preparatory phase of the session to minimize blinking and to keep their facial muscles relaxed. Additionally, researchers can emphasize the importance of staying still and provide practice sessions to help participants become aware of their movements, thereby reducing the likelihood of muscle artifacts affecting the EEG recordings.
We are seeking to appoint a part-time research assistant to help us recruit participants and conduct an experiment. In the current project, led by Jorge González Alonso and funded by the Research Council of Norway, we investigate language learning and the neurophysiological basis of multilingualism. To this end, we are conducting an electroencephalography (EEG) experiment.
Your work as a research assistant will be mentored and supervised primarily by Pablo Bernabeu, and secondarily by the head of our project and the directors of our lab.
Sometimes it’s useful to do a bibliometric analysis. To this end, the rscopus_plus functions (Bernabeu, 2024) extend the R package rscopus (Muschelli, 2022) to administer the search quota and enable specific searches and comparisons.
scopus_search_plus runs rscopus::scopus_search as many times as necessary based on the number of results and the search quota.
scopus_search_DOIs gets DOIs from scopus_search_plus, which can then be imported into a reference manager, such as Zotero, to create a list of references.
I would like to ask for advice regarding a custom plugin for a serial reaction time task, that was created by @vekteo, and is available in Gorilla, where the code can be edited and tested. By default, trials are self-paced, but I would need them to time out after 2,000 ms. I am struggling to achieve this, and would be very grateful if someone could please advise me a bit.
Longitudinal studies consist of several sessions, and often involve session session conductors. To facilitate the planning, registration and tracking of sessions, a session logbook becomes even more necessary than usual. To this end, an Excel workbook with conditional formatting can help automatise some formats and visualise the progress.
Below is an example that is available on OneDrive. To fully access this workbook, it may be downloaded via File > Save as > Download a copy.