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Figure 1

From: Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia

Figure 1

Study workflow. EEG data were acquired while participants listened to 30 one-minute tracks of a continuous narrative. Acoustic features were derived from the audio. Additionally, for each word in the stimulus, linguistic feature values were derived using natural language processing (NLP). Acoustic and linguistic features were used to estimate a TRF to map feature values to a participant’s EEG responses. The resulting TRF beta weights were then used as input to a ML-based classifier.

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