An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals

Vázquez, Manuel A.; Maghsoudi, Arash; Mariño, Inés P.

doi:10.3389/fnsys.2021.652662

Cited by 22 publications

(14 citation statements)

References 34 publications

(41 reference statements)

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“…Interpretable methods have been considered to better understand brain activities using EEG. Recently, algorithms providing clinical interpretation have been introduced in seizure prediction in epilepsy (Uyttenhove, 2020;Pinto et al, 2022) and schizophrenia detection (Vázquez et al, 2021). In addition, Three-stage algorithm was introduced to classify two spatial tasks (Yi et al, 2022).…”

Section: Discussion and Future Directions Brain Functions In Movement...mentioning

confidence: 99%

Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces

Plucknett

Giraldo

Bae

2022

Front. Hum. Neurosci.

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Decoding movement related intentions is a key step to implement BMIs. Decoding EEG has been challenging due to its low spatial resolution and signal to noise ratio. Metric learning allows finding a representation of data in a way that captures a desired notion of similarity between data points. In this study, we investigate how metric learning can help finding a representation of the data to efficiently classify EEG movement and pre-movement intentions. We evaluate the effectiveness of the obtained representation by comparing classification the performance of a Support Vector Machine (SVM) as a classifier when trained on the original representation, called Euclidean, and representations obtained with three different metric learning algorithms, including Conditional Entropy Metric Learning (CEML), Neighborhood Component Analysis (NCA), and the Entropy Gap Metric Learning (EGML) algorithms. We examine different types of features, such as time and frequency components, which input to the metric learning algorithm, and both linear and non-linear SVM are applied to compare the classification accuracies on a publicly available EEG data set for two subjects (Subject B and C). Although metric learning algorithms do not increase the classification accuracies, their interpretability using an importance measure we define here, helps understanding data organization and how much each EEG channel contributes to the classification. In addition, among the metric learning algorithms we investigated, EGML shows the most robust performance due to its ability to compensate for differences in scale and correlations among variables. Furthermore, from the observed variations of the importance maps on the scalp and the classification accuracy, selecting an appropriate feature such as clipping the frequency range has a significant effect on the outcome of metric learning and subsequent classification. In our case, reducing the range of the frequency components to 0–5 Hz shows the best interpretability in both Subject B and C and classification accuracy for Subject C. Our experiments support potential benefits of using metric learning algorithms by providing visual explanation of the data projections that explain the inter class separations, using importance. This visualizes the contribution of features that can be related to brain function.

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Section: Discussion and Future Directions Brain Functions In Movement...mentioning

confidence: 99%

Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces

Plucknett

Giraldo

Bae

2022

Front. Hum. Neurosci.

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“…Machine learning can be defined as computer models and algorithms that are able to automatically learn and adapt from data and experience without explicit instructions or human intervention [41][42][43][44][45][46][47][48]. As can be seen in Figure 5, machine learning methods can be classified into four categories: supervised learning, unsupervised learning deep learning, and reinforcement learning-the first three being more common.…”

Section: Machine Learning Algorithms Employed In Eeg Signal Classific...mentioning

confidence: 99%

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

et al. 2021

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In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

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“…Historically, many studies have developed automated SZ diagnosis approaches using EEG. These studies have involved both machine learning (ML) [7], [8] and deep learning (DL) methods [1], [9]. ML and DL each have their respective advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…ML and DL each have their respective advantages and disadvantages. Traditional ML studies use extracted features, like spectral power [7], [8] or inter-channel connectivity [7], [10], and offer enhanced explainability. Spectral features do offer some advantages over connectivity regarding explainability.…”

Section: Introductionmentioning

confidence: 99%

Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models

Ellis

Sattiraju

Miller

et al. 2022

Preprint

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Schizophrenia (SZ) is a neuropsychiatric disorder that adversely effects millions of individuals globally. Current diagnostic efforts are symptom based and hampered due to the variability in symptom presentation across individuals and overlap of symptoms with other neuropsychiatric disorders. This spawns the need for (1) biomarkers to aid with empirical SZ diagnosis and (2) the development of automated diagnostic approaches that will eventually serve in a clinical decision support role. In this study, we train random forest (RF) and support vector machine (SVM) models to differentiate between individuals with schizophrenia and healthy controls using spectral features extracted from resting state EEG data. We then perform two explainability analyses to gain insight into key frequency bands and channels. In our explainability analyses, we examine the reproducibility of SZ biomarkers across models with the goal of identifying those that have potential clinical implications. Our model performance results are well above chance level indicating the broader utility of spectral information for SZ diagnosis. Additionally, we find that the RF prioritizes the upper γ-band and is robust to loss of information from individual electrodes, while the SVM prioritizes the α and θ-bands and P4 and T8 electrodes. It is our hope that our findings will inform future efforts towards the empirical diagnosis of SZ and towards the development of clinical decision support systems for SZ diagnosis.

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An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals

Cited by 22 publications

References 34 publications

Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces

Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models

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