Opportunity Cost or Opportunity Lost: An Empirical Assessment of Ethical Concerns and Attitudes of EEG Neurofeedback Users

Kalokairinou, Louiza; Choi, Rebekah; Nagappan, Ashwini; Wexler, Anna

doi:10.1007/s12152-022-09506-x

Cited by 2 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Material costs can be substantial, for neurofeedback treatment typically takes several sessions to be completed (see tables in [25] for a summary of treat duration), requires the utilization of specialized equipment and time of an expert neurofeedback trainer. In a recent study, [26] found out that about 73% of users paid for neurofeedback out-of-pocket. As observed by [26], about 80% of neurofeedback users use it for indications not adequately supported by scientific evidence although more than half (57.6%) considered neurofeedback to be a scientifically well-established therapy.…”

Section: Plos Onementioning

confidence: 99%

“…In a recent study, [26] found out that about 73% of users paid for neurofeedback out-of-pocket. As observed by [26], about 80% of neurofeedback users use it for indications not adequately supported by scientific evidence although more than half (57.6%) considered neurofeedback to be a scientifically well-established therapy. This disconnection between scientific evidence and experiences of users can be considered part of the immaterial costs of neurofeedback.…”

Section: Plos Onementioning

confidence: 99%

See 1 more Smart Citation

Fat tails and the need to disclose distribution parameters of qEEG databases

Wood,

Willmes,

Koten

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Neurometry (a.k.a. quantitative EEG or qEEG) is a popular method to assess clinically relevant abnormalities in the electroencephalogram. Neurometry is based on norm values for the distribution of specific EEG parameters and believed to show good psychometric properties such as test-retest reliability. Many psychometric properties only hold under the Gaussian distribution and become problematic when distributions are fat-tailed. EEG signals are typically fat-tailed and do not show fast convergence to a Gaussian distribution. To circumvent this property of EEG, log-transformations have frequently, but not always been employed. In Monte Carlo simulations, we investigated the impact of fat-tails (i.e. deviations from Gaussian) on the cut-off criteria and changeability of what in neurometry is termed “abnormal EEG”. Even slight deviations from the Gaussian distribution as measured by skewness and kurtosis lead to large inflation in the number of false positive qEEG findings. The more stringent the cutoff value adopted, the larger the inflation. Moreover, “abnormal EEG” seems to recover spontaneously at rates not compatible with the alleged test-retest reliability of qEEG. Alternative methods should be employed to determine cut-off values for diagnostics purposes, since a large number of false positive results emerge even when slight deviations from the Gaussian distribution are present. We argue that distribution properties of qEEG databases should be disclosed in much more detail by commercial providers to avoid questionable research practices and promote diagnostic transparency. We provide recommendations for the improvement of psychometric properties of existing qEEG databases.

show abstract

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Fat tails and the need to disclose distribution parameters of qEEG databases

Wood,

Willmes,

Koten

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

Fat tails and the need to disclose distribution parameters of qEEG databases

Wood

Hinckeldey

Koten

et al. 2022

Preprint

View full text Add to dashboard Cite

Neurometry (a.k.a. quantitative EEG or qEEG) is a popular method to assess clinically relevant abnormalities in the electroencephalogram. Neurometry is based on norm values for the distribution of specific EEG parameters and believed to show good psychometric properties such as test-retest reliability. Many psychometric properties only hold under the Gaussian distribution and become problematic when distributions are fat-tailed. EEG signals are typically fat-tailed and do not show fast convergence to a Gaussian distribution. To circumvent this property of EEG, log-transformations have frequently, but not always been employed. In Monte Carlo simulations, we investigated the impact of fat-tails (i.e. deviations from Gaussian) on the cut-off criteria and changeability of what in neurometry is termed 'abnormal EEG'. Even slight deviations from the Gaussian distribution as measured by skewness and kurtosis lead to large inflation in the number of false positive qEEG findings. The more stringent the cutoff value adopted, the larger the inflation. For these reasons, we argue that distribution properties of qEEG databases should be disclosed in much more detail to avoid questionable research practices and promote diagnostic transparency. Moreover, 'abnormal EEG' seems to recover spontaneously at rates not compatible with the alleged test-retest reliability of qEEG. Alternative methods should be employed to determine cut-off values for diagnostics purposes, since a large number of false positive results emerge even when slight deviations from the Gaussian distribution are present. We provide recommendations for the improvement of psychometric properties of existing qEEG databases.

show abstract

Opportunity Cost or Opportunity Lost: An Empirical Assessment of Ethical Concerns and Attitudes of EEG Neurofeedback Users

Cited by 2 publications

References 51 publications

Fat tails and the need to disclose distribution parameters of qEEG databases

Fat tails and the need to disclose distribution parameters of qEEG databases

Fat tails and the need to disclose distribution parameters of qEEG databases

Contact Info

Product

Resources

About