Quantifying the time for accurate EEG decoding of single value-based decisions

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. Multivariate machine learning models have proven particularly effective in this domain, capturing intricate relationships among multiple variables simultaneously and enabling the accurate modeling of cerebral physiologic signals. These models facilitate the development of advanced diagnostic and prognostic tools, promote patient-specific interventions, and improve therapeutic outcomes. Additionally, machine learning models offer great flexibility, allowing different models to be combined synergistically to address complex challenges in sensor-based data analysis. Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. This review explores the use of multivariate machine learning models in cerebral physiology as a whole, with an emphasis on sensor-derived signals related to hemodynamics, cerebral oxygenation, metabolism, and other modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) where applicable. It will detail the operational principles, mathematical foundations, and clinical implications of these models, providing a deeper understanding of their significance in monitoring cerebral function.

Context changes retrieval of prospective outcomes during decision deliberation

Foreseeing the future outcomes is the art of decision-making. Substantial evidence shows that, during choice deliberation, the brain can retrieve prospective decision outcomes. However, decisions are seldom made in a vacuum. Context carries information that can radically affect the outcomes of a choice. Nevertheless, most investigations of retrieval processes examined decisions in isolation, disregarding the context in which they occur. Here, we studied how context shapes prospective outcome retrieval during deliberation. We designed a decision-making task where participants were presented with object-context pairs and made decisions which led to a certain outcome. We show during deliberation, likely outcomes were retrieved in transient patterns of neural activity, as early as 3 s before participants decided. The strength of prospective outcome retrieval explains participants' behavioral efficiency, but only when context affects the decision outcome. Our results suggest context imparts strong constraints on retrieval processes and how neural representations are shaped during decision-making.

The present and future of neural interfaces

The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.

Neural underpinnings of value-guided choice during auction tasks: An eye-fixation related potentials study

Values are attributed to goods during free viewing of objects which entails multi- and trans-saccadic cognitive processes. Using electroencephalographic eye-fixation related potentials, the present study investigated how neural signals related to value-guided choice evolved over time when viewing household and office products during an auction task. Participants completed a Becker-DeGroot-Marschak auction task whereby half of the stimuli were presented in either a free or forced bid protocol to obtain willingness-to-pay. Stimuli were assigned to three value categories of low, medium and high value based on subjective willingness-to-pay. Eye fixations were organised into five 800 ms time-bins spanning the objects total viewing time. Independent component analysis was applied to eye-fixation related potentials. One independent component (IC) was found to represent fixations for high value products with increased activation over the left parietal region of the scalp. An IC with a spatial maximum over a fronto-central region of the scalp coded the intermediate values. Finally, one IC displaying activity that extends over the right frontal scalp region responded to intermediate- and low-value items. Each of these components responded early on during viewing an object and remained active over the entire viewing period, both during free and forced bid trials. Results suggest that the subjective value of goods are encoded using sets of brain activation patterns which are tuned to respond uniquely to either low, medium, or high values. Data indicates that the right frontal region of the brain responds to low and the left frontal region to high values. Values of goods are determined at an early point in the decision making process and carried for the duration of the decision period via trans-saccadic processes.

The Decision Decoding ToolBOX (DDTBOX) - A Multivariate Pattern Analysis Toolbox for Event-Related Potentials

In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig 2004; Lopez-Calderon and Luck 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

Can Brain Waves Really Tell If a Product Will Be Purchased? Inferring Consumer Preferences From Single-Item Brain Potentials

Recent research has shown that event-related brain potentials (ERPs) recorded while participants view lists of different consumer goods can be modulated by their preferences toward these products. However, it remains largely unknown whether ERP activity specific to a single consumer item can be informative about whether or not this item will be preferred in a shopping context. In this study, we examined whether single-item ERPs could reliably predict consumer preferences toward specific consumer goods. We recorded scalp EEG from 40 participants while they were viewing pictures of consumer goods and we subsequently asked them to indicate their preferences for each of these items. Replicating previous results, we found that ERP activity averaged over the six most preferred products was significantly differentiated from ERP activity averaged across the six least preferred products for three ERP components: The N200, the late positive potential (LPP) and positive slow waves (PSW). We also found that using single-item ERPs to infer behavioral preferences about specific consumer goods led to an overall predictive accuracy of 71%, although this figure varied according to which ERPs were targeted. Later positivities such as the LPP and PSW yielded relatively higher predictive accuracy rates than the frontal N200. Our results suggest that ERPs related to single consumer items can be relatively accurate predictors of behavioral preferences depending on which type of ERP effects are chosen by the researcher, and ultimately on the level of prediction errors that users choose to tolerate.

Predicting risk decisions in a modified Balloon Analogue Risk Task: Conventional and single-trial ERP analyses

Event-related potential (ERP) has the potential to reveal the temporal neurophysiological dynamics of risk decision-making, but this potential has not been fully explored in previous studies. When predicting risk decision with ERPs, most studies focus on between-trial analysis that reflects feedback learning, while within-trial analysis that could directly link option assessment with behavioral output has been largely ignored. Suitable task design is crucial for applying within-trial prediction. In this study, we used a modified version of the classic Balloon Analogue Risk Task (BART). In each trial of the task, participants made multiple rounds of decisions between a risky option (pump up the balloon) and a safe option (cash out). Behavioral results show that as the level of economic risk increased, participants were less willing to make a risky decision and also needed a longer response time to do so. In general, the ERP results showed distinct characteristics compared with previous findings based on between-trial prediction, particularly about the role of the P1 component. Specifically, both the P1 (amplitude and latency) and P3 (amplitude) components evoked by current outcomes predicted subsequent decisions. We suggest that these findings indicate the importance of selective attention (indexed by the P1) and motivational functions (indexed by the P3), which may help clarify the cognitive mechanism of risk decision-making. The theoretical significance of these findings is discussed.

Neural correlates of economic value and valuation context: an event-related potential study

The value of environmental cues and internal states is continuously evaluated by the human brain, and it is this subjective value that largely guides decision making. The present study aimed to investigate the initial value attribution process, specifically the spatiotemporal activation patterns associated with values and valuation context, using electroencephalographic event-related potentials (ERPs). Participants completed a stimulus rating task in which everyday household items marketed up to a price of £4 were evaluated with respect to their desirability or material properties. The subjective values of items were evaluated as willingness to pay (WTP) in a Becker-DeGroot-Marschak auction. On the basis of the individual's subjective WTP values, the stimuli were divided into high- and low-value items. Source dipole modeling was applied to estimate the cortical sources underlying ERP components modulated by subjective values (high vs. low WTP) and the evaluation condition (value-relevant vs. value-irrelevant judgments). Low-WTP items and value-relevant judgments both led to a more pronounced N2 visual evoked potential at right frontal scalp electrodes. Source activity in right anterior insula and left orbitofrontal cortex was larger for low vs. high WTP at ∼200 ms. At a similar latency, source activity in right anterior insula and right parahippocampal gyrus was larger for value-relevant vs. value-irrelevant judgments. A stronger response for low- than high-value items in anterior insula and orbitofrontal cortex appears to reflect aversion to low-valued item acquisition, which in an auction experiment would be perceived as a relative loss. This initial low-value bias occurs automatically irrespective of the valuation context. NEW & NOTEWORTHY We demonstrate the spatiotemporal characteristics of the brain valuation process using event-related potentials and willingness to pay as a measure of subjective value. The N2 component resolves values of objects with a bias toward low-value items. The value-related changes of the N2 component are part of an automatic valuation process.

Neural Mechanisms of Attentional Switching Between Pain and a Visual Illusion Task: A Laser Evoked Potential Study

Previous studies demonstrated that pain induced by a noxious stimulus during a distraction task is affected by both stimulus-driven and goal-directed processes which interact and change over time. The purpose of this exploratory study was to analyse associations of aspects of subjective pain experience and engagement with the distracting task with attention-sensitive components of noxious laser-evoked potentials (LEPs) on a single-trial basis. A laser heat stimulus was applied to the dorsum of the left hand while subjects either viewed the Rubin vase-face illusion (RVI), or focused on their pain and associated somatosensory sensations occurring on their stimulated hand. Pain-related sensations occurring with every laser stimulus were evaluated using a set of visual analogue scales. Factor analysis was used to identify the principal dimensions of pain experience. LEPs were correlated with subjective aspects of pain experience on a single-trial basis using a multiple linear regression model. A positive LEP component at the vertex electrodes in the interval 294–351 ms (P2) was smaller during focusing on RVI than during focusing on the stimulated hand. Single-trial amplitude variations of the P2 component correlated with changes in Factor 1, representing essential aspects of pain, and inversely with both Factor 2, accounting for anticipated pain, and the number of RVI figure reversals. A source dipole located in the posterior region of the cingulate cortex was the strongest contributor to the attention-related single-trial variations of the P2 component. Instantaneous amplitude variations of the P2 LEP component during switching attention towards pain in the presence of a distracting task are related to the strength of pain experience, engagement with the task, and the level of anticipated pain. Results provide neurophysiological underpinning for the use of distraction analgesia acute pain relief.