FIREFLY NEUROSCIENCE INC
Filed Sep 27, 2024.
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period unavailable · Filed Sep 27, 2024
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Analyze - The BNA Algorithm
After the recording, the artifact-free EEG data is imported into the BNA Platform and is automatically analyzed by the algorithm (the “BNA Algorithm”). The BNA Algorithm does so by detecting and quantifying the characteristics of EEG and ERP peaks (AOB task: P50, N100, P200, P3a, P3b; VGNG task: P200, N200, P3a, P3b). The BNA Algorithm leverages Firefly’s normative database to extract clinically meaningful information from EEG data.
EEG recordings are high-dimensional and complex. This is the result of the dynamic spatiotemporal nature of the neurophysiological signals. For example, the event-related potential wave measured at first, at a relatively focal location in time and space on the scalp, and then it evolves and propagates in the cortex while its amplitude changes simultaneously.
Traditional methods for EEG and ERP analysis follow waveform morphology over time at selected electrode locations, using either time-domain or frequency-domain tools, while neglecting the spatiotemporal dynamics associated with the electric field at the scalp. Moreover, focusing on a specific electrode or subset of electrodes, the traditional methods ignore the expected variability between patients in terms of where the ERP component appears on the scalp due to natural differences between cortex and scalp anatomy. The aim of the BNA Algorithm is to improve the accuracy of the ERP component detection by considering the spatiotemporal nature of the signal. It achieves this improvement by parceling the EEG activity into major spatiotemporal events - ERP peaks and their surrounding - spatiotemporal parcels.
The key strengths of the BNA Algorithm are:
| 1. | Adaptive peak detection - the ERP peaks are detected in a more patient-specific way, by incorporating some degree of freedom in both time and space in a way that is proportional to the normal variability in time and space of the ERP peaks. |
| 2. | Displaying ERP-peak location - thanks to the adaptive peak detection by the BNA Algorithm, the ERP peak is identified and displayed in the BNA report on a topographical map together with the group ERP-peak position. This adds clinically useful information to the physician. |
| 3. | Neural-Consistency score - another unique feature of the BNA report is the presentation of the Neural-Consistency score. Since this is a differentiating score, the Company will describe here shortly what this score is and why Firefly believes it is important. |
Since the ERP signal is an average of multiple single-trial ERP signals which can differ largely from each other, assessing the consistency between them may provide complementary functional information to the average measure. As part of the new peak-analysis, the algorithm calculates a new score - ‘Neural Consistency’ based on the similarity of the amplitude activation between single-ERP trials. The score is calculated based on averaging the inter-single trials variability of the ERP-peak and its surrounding points.
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