scholarly journals A Comparison of fMRI and Behavioral Models for Predicting Inter-Temporal Choices

2019 ◽  
Author(s):  
Felix G. Knorr ◽  
Philipp T. Neukam ◽  
Juliane H. Fröhner ◽  
Holger Mohr ◽  
Michael N. Smolka ◽  
...  
Keyword(s):  
Cross Validation ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Behavioral Model ◽  
Support Vector ◽  
Behavioral Models ◽  
Single Trial ◽  
Subjective Value ◽  
The Brain ◽  
Time Point

AbstractIn an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt to build a classifier that would ideally outperform established behavioral model given that it has access to brain activity specific to a single trial. Such methods could allow for future investigations of state-like factors that influence IteCh choices.To investigate this, coefficients of a GLM with one regressor per trial were used as features for a support vector machine (SVM) in combination with a searchlight approach for feature selection and cross-validation. We then compare the results to the performance of four different behavioral models.We found that the behavioral models reached mean accuracies of 90% and above, while the fMRI model only reached 54.84% at the best location in the brain with a spatial distribution similar to the well-known value-tracking network. This low, though significant, accuracy is in line with simulations showing that classifying based on signals with realistic correlations with subjective value produces comparable, low accuracies. These results emphasize the limitations of fMRI recordings from single events to predict human choices, especially when compared to conventional behavioral models. Better performance may be obtained with paradigms that allow the construction of miniblocks to improve the available signal-to-noise ratio.

scholarly journals Single-Trial Decoding of Scalp EEG under Natural Conditions

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Greta Tuckute ◽  
Sofie Therese Hansen ◽  
Nicolai Pedersen ◽  
Dea Steenstrup ◽  
Lars Kai Hansen
Keyword(s):  
Signal To Noise Ratio ◽  
Mental States ◽  
Small Samples ◽  
Support Vector ◽  
Large Set ◽  
Learning Approaches ◽  
Single Trial ◽  
Scalp Eeg ◽  
Dry Electrode ◽  
Sensitivity Maps

There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding.

scholarly journals Altered resting-state network dynamics in Schizophrenia

2020 ◽  
Author(s):  
Ana Rita Lopes ◽  
Anna Sardinha Letournel ◽  
Joana Cabral
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Phase Locking ◽  
Bold Signal ◽  
Functional Magnetic Resonance ◽  
Probability Of Occurrence ◽  
Magnetic Resonance Data ◽  
Resonance Data ◽  
The Brain ◽  
Time Point

Schizophrenia remains a poorly understood disease, hence the interest in assessing and indirectly characterizing brain activity and connectivity. This paper aims to search for potential biomarkers in schizophrenia with functional magnetic resonance data, between subjects in the resting state. Firstly, we used fMRI from an open database, SchizConnect, of 48 subjects, in which 27 were control subjects, with no apparent disease and the others 21 were patients with schizophrenia. With the SPM tool, we proceeded to manually pre-process the images obtained, at the risk of having influenced the final results. Then, with the AAL atlas as a reference, we divided the brain into 116 areas. Then, brain activity in these areas were analysed, using the LEiDA method, which aims to characterize brain activity at each time point t by phase locking patterns of the BOLD signal. After the application of LEiDA, brain activity was evaluated based on trajectories and bar graphs of functional connectivity states in which the probability of occurrence and their dwell time were calculated for each state. It was also found that the visual cortex was the subsystem that showed significantly more probability of occurrence in schizophrenia patients to be assessed, and may correspond to symptoms of hallucinations by the patients with schizophrenia.

scholarly journals Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Boniadi Naieni
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Efficient ◽  
The Brain

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

scholarly journals Single-trial neural dynamics influence auditory category learning

2020 ◽  
Author(s):  
Kelsey Mankel ◽  
Philip I. Pavlik ◽  
Gavin M. Bidelman
Keyword(s):  
Computational Modeling ◽  
Category Learning ◽  
Brain Activity ◽  
Educational Data Mining ◽  
Training Session ◽  
Performance Model ◽  
Learning Performance ◽  
Single Trial ◽  
Brain Behavior ◽  
The Brain

AbstractPercepts are naturally grouped into meaningful categories to process continuous stimulus variations in the environment. Theories of category acquisition have existed for decades, but how they arise in the brain due to learning is not well understood. Here, advanced computational modeling techniques borrowed from educational data mining and cognitive psychology were used to trace the development of auditory categories within a short-term training session. Nonmusicians were rapidly trained for 20 min on musical interval identification (i.e., minor and major 3rd interval dyads) while their brain activity was recorded via EEG. Categorization performance and neural responses were then assessed for the trained (3rds) and novel untrained (major/minor 6ths) continua. Computational modeling was used to predict behavioral identification responses and whether the inclusion of single-trial features of the neural data could predict successful learning performance. Model results revealed meaningful brain-behavior relationships in auditory category learning detectible on the single-trial level; smaller P2 amplitudes were associated with a greater probability of correct interval categorization after learning. These findings highlight the nuanced dynamics of brain-behavior coupling that help explain the temporal emergence of auditory categorical learning in the brain.

scholarly journals The processing of color preference in the brain

2018 ◽  
Author(s):  
Chris Racey ◽  
Anna Franklin ◽  
Chris M. Bird
Keyword(s):  
Brain Activity ◽  
Blood Oxygen Level Dependent ◽  
Color Preference ◽  
Blood Oxygen Level ◽  
Neural Basis ◽  
Color Preferences ◽  
Subjective Value ◽  
Value Judgements ◽  
Automatic Encoding ◽  
The Brain

AbstractDecades of research has established that humans have preferences for some colors (e.g., blue) and a dislike of others (e.g., dark chartreuse), with preference varying systematically with variation in hue (e.g., Hurlbert & Owen, 2015). Here, we used functional MRI to investigate why humans have likes and dislikes for simple patches of color, and to understand the neural basis of preference, aesthetics and value judgements more generally. We looked for correlations of a behavioural measure of color preference with the blood oxygen level-dependent (BOLD) response when participants performed an irrelevant orientation judgement task on colored squares. A whole brain analysis found a significant correlation between BOLD activity and color preference in the posterior midline cortex (PMC), centred on the precuneus but extending into the adjacent posterior cingulate and cuneus. These results demonstrate that brain activity is modulated by color preference, even when such preferences are irrelevant to the ongoing task the participants are engaged. They also suggest that color preferences automatically influence our processing of the visual world. Interestingly, the effect in the PMC overlaps with regions identified in neuroimaging studies of preference and value judgements of other types of stimuli. Therefore, our findings extends this literature to show that the PMC is related to automatic encoding of subjective value even for basic visual features such as color.

The Identification of EEG Feature Evoked by Imaginary Movement

2013 ◽  
Vol 427-429 ◽  
pp. 2059-2063
Author(s):  
Xiao Yan Qiao ◽  
Chun Hui Wang
Keyword(s):  
Cross Validation ◽  
Signal To Noise Ratio ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signal ◽  
Signal To Noise ◽  
Rbf Kernel ◽  
Maximum Mutual Information ◽  
Eeg Features

Aiming to the ERD/ERS phenomenon of left-right hand imaginary movement, this paper presents a method of wavelet transform combined with statistical analysis to extract EEG features evoked by imaginary movement. And the features were classified using the support vector machine based on RBF kernel and cross-validation accuracy (CVA) method. The results have shown that this method can perform effectively to extract features and reflect ERS and ERD characteristics of EEG signal. The accuracy of classification can reach 90% within the time costing 3.5 seconds. The highest signal to noise ratio is 1.445, and the maximum mutual information is 0.645bit. The results can meet the real-time brain-computer interface system.

scholarly journals Single-Trial Decoding of Scalp EEG Under Natural Conditions

2018 ◽  
Author(s):  
Greta Tuckute ◽  
Sofie Therese Hansen ◽  
Nicolai Pedersen ◽  
Dea Steenstrup ◽  
Lars Kai Hansen
Keyword(s):  
Signal To Noise Ratio ◽  
Mental States ◽  
Small Samples ◽  
Support Vector ◽  
Large Set ◽  
Learning Approaches ◽  
Single Trial ◽  
Scalp Eeg ◽  
Dry Electrode ◽  
Sensitivity Maps

ABSTRACTThere is significant current interest in decoding mental states from electro-encephalography (EEG) recordings. EEG signals are subject-specific, sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that Support Vector Machine (SVM) classifiers trained on a relatively small set of de-noised (averaged) pseudo-trials perform on par with classifiers trained on a large set of noisy single-trial samples. For visualization of EEG signatures exploited by SVM classifiers, we propose a novel method for computing sensitivity maps of EEG-based SVM classifiers. Moreover, we apply the NPAIRS resampling framework for estimation of map uncertainty and show that effect sizes of sensitivity maps for classifiers trained on small samples of de-noised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudo-trial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization and unbiased performance evaluation in machine learning approaches for brain decoding.

scholarly journals Covert Intention to Answer “Yes” or “No” Can Be Decoded from Single-Trial Electroencephalograms (EEGs)

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Jeong Woo Choi ◽  
Kyung Hwan Kim
Keyword(s):  
Interpersonal Communication ◽  
Self Regulation ◽  
Neural Mechanism ◽  
Support Vector ◽  
Alpha Band ◽  
Single Trial ◽  
Theta Band ◽  
Common Spatial Pattern ◽  
Time Frequency ◽  
The Brain

Interpersonal communication is based on questions and answers, and the most useful and simplest case is the binary “yes or no” question and answer. The purpose of this study is to show that it is possible to decode intentions on “yes” or “no” answers from multichannel single-trial electroencephalograms, which were recorded while covertly answering to self-referential questions with either “yes” or “no.” The intention decoding algorithm consists of a common spatial pattern and support vector machine, which are employed for the feature extraction and pattern classification, respectively, after dividing the overall time-frequency range into subwindows of 200 ms × 2 Hz. The decoding accuracy using the information within each subwindow was investigated to find useful temporal and spectral ranges and found to be the highest for 800–1200 ms in the alpha band or 200–400 ms in the theta band. When the features from multiple subwindows were utilized together, the accuracy was significantly increased up to ∼86%. The most useful features for the “yes/no” discrimination was found to be focused in the right frontal region in the theta band and right centroparietal region in the alpha band, which may reflect the violation of autobiographic facts and higher cognitive load for “no” compared to “yes.” Our task requires the subjects to answer self-referential questions just as in interpersonal conversation without any self-regulation of the brain signals or high cognitive efforts, and the “yes” and “no” answers are decoded directly from the brain activities. This implies that the “mind reading” in a true sense is feasible. Beyond its contribution in fundamental understanding of the neural mechanism of human intention, the decoding of “yes” or “no” from brain activities may eventually lead to a natural brain-computer interface.

scholarly journals Decoding explicit and implicit representations of health and taste attributes of foods in the human brain

2021 ◽  
Author(s):  
Elektra Schubert ◽  
Daniel Rosenblatt ◽  
Djamila Eliby ◽  
Yoshihisa Kashima ◽  
Hinze Hogendoorn ◽  
...  
Keyword(s):  
Brain Activity ◽  
Event Related Potentials ◽  
Food Choices ◽  
Support Vector ◽  
Snack Foods ◽  
Related Potentials ◽  
Obesity Interventions ◽  
Cue Processing ◽  
The Brain ◽  
Healthy Participants

Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) occurring in the first second of food cue processing to predict ratings of tastiness and healthiness. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness, whereas in Experiment 2, 89 healthy participants indicated their desire to consume snack foods, with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Sign in / Sign up

Export Citation Format

Share Document