My ISNR Education Content

2020: Automatic De-Artifacting in Normative qEEG Databases-Does Reality follow the Hype (Plenary)

Current Status
Not Enrolled
Price
30
Get Started

Presented by Marco Rotonda: Advances in the field of automated de-artifacting algorithms for electroencephalographic (EEG) signals have provided powerful tools to significantly speed-up data processing (Makeig et al., 1996; Delorme et al., 2007; Nolan, 2010; Mullen et al., 2015; Blum, 2019). Nonetheless, the efficacy of such, sometimes pre-implemented algorithms in marketed solutions for qEEG analysis still needs to be addressed. Here, we directly compared the performance of automatic algorithms implemented in different normative qEEG DataBases (DBs) currently available on the market (Neuroguide, qEEG-Pro, and iMediSync) with a manual artifact-rejection approach.

MATERIALS & METHODS (1): Raw, eyes closed resting-state EEG data from four exemplar subjects were fed into the pre-implemented, automated artifact-detection, and rejection or correction pipelines of the three DBs. Additionally, manual artifact-detection and rejection was performed separately. Thereafter, the absolute power values for five different frequency-bands (FBs; Delta, Theta, Alpha, Beta1, Beta2) were computed at each electrode position (19 channels, 10/20 layout) within each DB. The resulting values were assessed in a mixed-design ANOVA (within-subject factors: 2 (automated cleaning vs. manual rejection) x 3 (DBs) x 5 (FBs); between-subject factor: electrode).

RESULTS (1): Results suggested significant effects between DBs, which were driven by the amplitude of the absolute power values computed. Specifically, we found a main effect (ME) for DB and FB. Additionally, we found significative interactions between DB x Cleaning, Cleaning x FBs and DB x Cleaning x FBs.

INTERIM CONCLUSION (1): Taken alone, these results provide striking evidence that artifact-detections algorithm implemented in the selected DBs hugely impact data interpretation and can hence eventually diverge diagnostic interpretations when absolute power values are taken into consideration.

MATERIALS & METHODS (2): In order to correct for normalization biases across DB outputs in the current analyses, absolute power measures were transformed into Z-Score values across the whole dataset. Hereby, we sought to eliminate false positives in our statistical evaluation due to the different implementation of the power computation algorithms between DBs (White, 2003).

RESULTS (2): The mixed-design ANOVA suggested significant differences as a function of Electrode (DB x Electrode; FBs x Electrode).

INTERIM CONCLUSION (2): these results suggest that normalization processes can effectively reduce erroneous statistical differences due to differential implementation amplitude computations. Nonetheless, significant Electrode x DB interactions persist.
GENERAL DISCUSSION: Taken together, these preliminary findings underline the existent pitfalls regarding de-artifacting algorithms across DBs. Specifically, differential implementation of absolute power value computation greatly diverges between the present DBs investigated (similar: White, 2003; contrary: Thatcher & Lubar, 2009 and Keizer, 2018). Moreover, normalization of the data still produced significant differences across DBs.

CONCLUSION, OPEN QUESTIONS & FUTURE DIRECTIONS:
Several open questions remain to be addressed. First, the impact of differential cohorts employed between DBs for normalization processes has not been directly assessed in the present study. Second, whether automatic de-artifacting algorithms consistently out-perform manual data cleaning, needs to be further investigated. Third, the qualitative differences of the automatic de-artifacting algorithms employed by different DBs need further comparisons. We are aware of the limited sample size and aim at increasing the sample size.

Current Status
Not Enrolled
Price
30
Get Started

We’ve Moved…

To accommodate the organization’s growing needs, we have decided to move our office to a new location.

2146 Roswell Road

Suite 108, PMB 736

Marietta, GA 30062

USA

Scroll to Top

Are you having problems clicking next on the membership form?

You must have 3rd party cookies set to “Always Accept.”

Internet Explorer 7 on Windows

  • Click the “Tools” menu
  • Click “Internet Options”
  • Select the “Privacy” tab
  • Option 1: To enable third-party cookies for all sites
  • Click “Advanced”
  • Select “Override automatic cookie handling”
  • Select the “Accept” button under “Third-party Cookies” and click “OK”

Firefox 3 on Windows

  • Click the “Tools” menu
  • Click “Options…”
  • Select the “Privacy” menu
  • Make sure “Keep until” is set to “they expire”
  • Option 1: To enable third party cookies for ALL sites: Make sure “Accept third-party cookies” is checked

Safari on Apple OS X:

  • Click the “Safari” menu
  • Click “Preferences…”
  • Click the “Security” menu
  • For “Cookies and website data” unselect “Block all cookies”
  • For “Website tracking”, unselect “Prevent cross-site tracking”
Safari enable cookies for membership purchase.

Firefox 3 on Apple OS X:

  • Click the “Firefox” menu
  • Click Preferences…
  • Click the Privacy menu
  • Make sure “Keep until” is set to “they expire”
  • Option 1: To enable third-party cookies for ALL sites: Make sure “Accept third-party cookies” is checked

Google Chrome on Windows

  • Select the Wrench (spanner) icon at the top right
  • Select “Options”
  • Select the “Under the Hood” tab
  • Select “Allow all cookies” under “Cookie Settings” and click “Close”

Internet Explorer 6 on Windows

  • Click the “Tools” menu
  • Click “Internet Options”
  • Select the “Privacy” tab
  • Move the settings slider to “Low” or “Accept all cookies”
  • Click “OK”

Opera 9 on Windows

  • Click the “Tools” menu
  • Click “Preferences…”
  • Click the “Advanced” tab
  • Select “Cookies” on the left list
  • Make sure “Accept cookies” is selected and uncheck “Delete new cookies when exiting Opera”
  • Click “OK”