EEG analysis based on dynamic visual stimuli: best practices in analysis of sign language data
EEG analysis based on dynamic visual stimuli: best practices in analysis of sign language data

Summary/Abstract: This paper reviews best practices for experimental design and analysis for sign language research using neurophysiological methods, such as electroencephalography (EEG) and other methods with high temporal resolution, as well as identifies methodological challenges in neurophysiological research on natural sign language processing. In particular, we outline the considerations for generating linguistically and physically well-controlled stimuli accounting for 1) the layering of manual and non-manual information at different timescales, 2) possible unknown linguistic and non-linguistic visual cues that can affect processing, 3) variability across linguistic stimuli, and 4) predictive processing. Two specific concerns with regard to the analysis and interpretation of observed event related potential (ERP) effects for dynamic stimuli are discussed in detail. First, we discuss the “trigger/effect assignment problem”, which describes the difficulty of determining the time point for calculating ERPs. This issue is related to the problem of determining the onset of a critical sign (i.e., stimulus onset time), and the lack of clarity as to how the border between lexical (sign) and transitional movement (motion trajectory between individual signs) should be defined. Second, we discuss possible differences in the dynamics within signing that might influence ERP patterns and should be controlled for when creating natural sign language material for ERP studies. In addition, we outline alternative approaches to EEG data analyses for natural signing stimuli, such as the timestamping of continuous EEG with trigger markers for each potentially relevant cue in dynamic stimuli. Throughout the discussion, we present empirical evidence for the need to account for dynamic, multi-channel, and multi-timescale visual signal that characterizes sign languages in order to ensure the ecological validity of neurophysiological research in sign languages.

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