TY  - JOUR
AU  - Slav Danev
AU  - Jonathan RT Lakey
AU  - Krista Casazza
PY  - 2025
DA  - 2025/08/18
TI  - Advancing Clinical Neuroassessment: The BrainView ERP Platform in Schizophrenia
JO  - Neurology and Neuroscience
VL  - 6
IS  - 7
AB  - Event-related potentials (ERPs) offer objective insights into brain function, with alterations in
components such as the N100, P300, and N400 commonly observed in individuals with schizophrenia.
However, scalable, automated ERP-based diagnostic tools remain limited in clinical practice. This study
evaluated the diagnostic performance of the BrainView ERP platform in distinguishing individuals with
schizophrenia from healthy controls using ERP biomarkers. A total of 334 participants were enrolled,
including 94 individuals with clinically diagnosed schizophrenia and 240 age- and sex-matched healthy
controls. All participants completed a standardized ERP protocol utilizing visual and semantic oddball
paradigms. ERP features (latency and amplitude) from nine scalp regions (Fp1, Fp2, F7, F3, Fz, F4,
F8, P3, P4) were recorded and analyzed, with particular focus on P300, N100, and N400 components.
A machine learning classifier trained on these features was used to differentiate diagnostic groups.
Receiver operating characteristic (ROC) analysis assessed model performance. The BrainView platform
achieved a sensitivity of 0.88 and specificity of 0.90, with an area under the ROC curve (AUC) of
0.91, indicating excellent discriminative ability. ERP abnormalities in the schizophrenia group included
reduced P300 amplitudes, delayed N400 latencies, and attenuated N100 responses—consistent with
deficits in attention, sensory gating, and semantic processing. These results compare favorably with
prior studies, confirming BrainView’s alignment with established ERP biomarkers. The BrainView ERP
platform demonstrates high diagnostic accuracy in identifying schizophrenia, offering an efficient,
objective, and scalable tool for neuropsychiatric assessment. By integrating validated ERP biomarkers
with machine learning-based classification, BrainView advances the potential for early detection and
personalized treatment strategies in clinical psychiatry.
SN  - 2692-7918
UR  - https://dx.doi.org/10.33425/2692-7918.1106
DO  - 10.33425/2692-7918.1106