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Obesity and overweight are frequently prescribed for dysfunction in food-intake behavior. Due to the widely prevalence
of obesity in last year’s, there is demand for more studies which are aimed to modify the food-intake behavior. For
the past decades many researches has applied in modify food-intake by brain training or stimulation. This review fo...
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... However, it is important to note a significant limitation of LIME. In the baseline LIME's perturbation technique disrupts the temporal relationships present in signals, such as ECG and EEG [15], potentially resulting in unlikely and unrealistic data points [16]. ...
Interpreting machine learning models is facilitated by the widely employed locally interpretable model-agnostic explanation (LIME) technique. However, when extending LIME to signal data, its credibility falters due to perturbation techniques used to generate local datasets. These techniques disrupt temporal dependencies among features, leading to unrealistic data points and potentially misleading explanations. Additionally, LIME faces instability and local fidelity issues, limiting its suitability for real-world applications. The absence of a dedicated LIME package tailored for interpreting signal data further diminishes comprehensibility, especially when applied to models trained on such data. In this paper, we introduce Signal-based LIME (Sig-LIME) to address these limitations. Sig-LIME leverages a novel data generation technique that captures temporal dependence among features, enhancing credibility and stability. It combines a random forest model and heatmaps to provide illuminating explanations for predictions drawn from electrocardiogram (ECG) signals, improving model transparency. Empirical findings underscore the enhanced interpretability and comprehension of model predictions attained by Sig-LIME compared to baseline LIME. Our quantitative evaluation based on an analysis of variance (ANOVA) framework, reveals a notable improvement in stability with Sig-LIME, evidenced by an f-statistic of 0.0 and p-values of 1, indicating a complete absence of variation between multiple runs. Regarding local fidelity, Sig-LIME surpasses the baseline LIME, exhibiting a lower average Euclidean distance of 0.49 compared to 17.24. Sig-LIME excels in generating data more akin to the original, achieving remarkable stability and significantly enhancing credibility and local fidelity in the explanations it generates.
... The procedure of the EEG-NFT (taken fromAlhiyali et al., 2018) ...
This dissertation, after a critical review of the extant neurofeedback training (NFT) literature in sport, has identified some evidence for NFT as an approach that should enhance sporting performance in some circumstances; however, this evidence is on shaky grounds. A subsequent study tested the effectiveness of NFT on attention and reaction time of athletes, but found a smaller effect than what was found in the literature. This dissertation also highlights the debates on NFT and its effectiveness, and offers a solution by developing an allostasis four-stage model of NFT.
... However, the experiment works in the literature of NF used the FMRI-NF device to PFC stimulation in excess weight individuals to change food intake behavior [11], [12], while the EEG-NF has not yet been applied to PFC stimulation in these cases, although the EEG device is affordable and easy handle compared with FMRI. Also, the total number of EEG-NF studies in the eating behavior area are very scares compared with other techniques [13]. Therefore, this study hypothesizes that EEGneurofeedback stimulating the prefrontal activity and leads to modifying the general symptoms of food intake behaviors in experiment participants. ...
Background: This study aims to investigate the effects of visual neurofeedback stimulation on the brain activity in overweight cases. The neuroscience studies indicated the personal decision about eating under the impact of environmental factors such as (visually, smelling, tasting) is related to neural activity of the prefrontal lobe of the brain. Therefore, there were many attempts to modify the food intake behavior in overweight cases through the stimulation of the prefrontal cortex. However, the empirical viewing of EEG-neurofeedback experiments has not explicated the details about the effect of the EEG-NF, the specificity of positive treatment effects remains in a challenging scope.Methods: This study is a cue-exposure EEG-NF experiment to verify the hypothesis of effecting the EEG-NF on the electrical activity of PFC and modifying the general symptoms of food intake behavior in overweight cases. Twenty-four individuals were recruited as participants for this study. These participants were assigned randomly into two groups; the EX-Group (N=12) enrolled in 8 sessions of the EEG-NF experiment, and the C-Group (N=12) was listed in no EEG-NF sessions. The participants provided researchers with a self-report questionnaire relating to their observation of general symptoms of food intake behavior, and EEG signals recordings into the pre and posts stimulation phase. The power spectral density (PSD) method was applied for EEG parameters extraction.Results: The results of a two-way analysis of variance (ANOVA) explained that a significant variation in variables between the two groups after the EEG-NF experiment. The analysis of the quantitative variables indicated that the effect of EEG-NF experiment was a significant decrement in EEG power bands which significantly influenced changing the median of self-report questionnaire responses that is related to general symptoms of food intake behavior.Conclusions: This study provides preliminary support for the therapeutic potential of EEG-NF experiment that targets the prefrontal cortex, to influence neural processes underlying food intake behavior in overweight cases.
... However, The EEG-Neurofeedback (EEG-NF) is one of brain stimulation devices that operates a real-time of EEG signal to modify brain activity [8]. Despite that it is a non-surgical interventional, the EEG-NF hasn't been applied yet in PFC stimulation for excess weight cases [9]. The aim of this study is a preliminary examination of prefrontal cortex activity after NF stimulation sessions by quantitative assessment of EEG signals. ...
Food intake regulation is considered the key to weight control and overweight prevention. The brain activity in PreFrontal Cortex (PFC) plays a role in food intake behaviors. Most of the previous studies were aimed to PFC stimulation in overweight cases to modify the food intake behaviors. The EEG-neurofeedback is one of the brain stimulation techniques; therefore, this study aims to find the effect of EEG-NF stimulation on PFC function by EEG features analysis. For the purpose of analysis, the theta\beta ratio was extracted from ten healthy overweight participators in this study. All participants were divided into two groups, experimental group and control group with two phase-terms, pre and post-stimulation phase. The experiments were run using EEG-NF device. The results in this study indicate that success of EEG-Neurofeedback in PFC stimulation of overweight cases may have an influence on changing the food intake behavior.
