Technische Universität MünchenA research project at our university investigates how #BrainSignals can be translated into digital commands, aiming to enable people with #paralysis to interact with computers or #RoboticSystems: http://go.tum.de/611249
📷A. Eckert
One MomentCrystal L. Fedeli explains the neurochemical reality: in alcohol dependency, endorphins create signals that alcohol is needed to survive, forming neural pathways. Whatever fires together wires together. This is the brain's process—real and changeable with new connections. #NeuroHealers #BrainSignals #SurvivalToThriving
To hear the complete discussion: https://youtu.be/H2iNZBgDp_U?si=Ff22x4TlEg_-4O43
purescience_newsResearchers predict romantic attraction using brain signals and AI #Science #ComputerScience #ArtificialIntelligence #BrainSignals #RomanticAttraction #AIResearch
https://purescience.news/article?id=959393
https://purescience.news/article?id=959393
Researchers predict romantic attraction using brain signals and AI
A small study in Israel developed a machine learning model that achieved moderate to high accuracy in predicting romantic attraction and rejection in a simulated dating app scenario. The researchers also found that the model was better able to predict the romantic emotions of picky participants than for those who were less fussy about their romantic interests, suggesting that picky individuals might exhibit stronger, more distinct brain activity related to romantic preferences. The paper was published in Computers in Biology and Medicine. Romantic love is an important aspect of human relationships. For many, it is the most important aspect of their lives. Romantic love promotes intimacy, bonding, and long-term pair connections that support both individual well-being and social stability. Across cultures, romantic love is linked to companionship, emotional security, and the motivation to form families. Psychologically, it engages brain systems tied to reward and attachment, which can foster resilience and life satisfaction. In modern societies, however, the ways people meet potential partners have changed significantly. Dating apps provide a new method for initiating relationships, expanding social networks beyond traditional circles of friends, workplaces, or communities. They can increase efficiency by matching people based on preferences, values, or location, and they offer opportunities to individuals who might otherwise struggle with limited social exposure. At the same time, dating apps can sometimes promote superficial judgments, encourage serial dating, or create unrealistic expectations. Study authors Dor Zazon and Nir Nissim wanted to explore whether machine learning can be used to accurately infer an individual’s romantic emotions based on the analysis of their neural responses to fictional dating profiles using electroencephalography (EEG). They aimed to predict attraction by analyzing event-related potentials (ERPs)—the brain’s specific electrical responses to stimuli like seeing a face—which were captured by the EEG. The 61 participants were students at Ben-Gurion University of the Negev in Israel, aged 23 to 32, and included 31 women. Study authors used 400 free images of people (200 men, 200 women) between 20 and 30 years of age downloaded from the internet to serve as visual stimuli. Each participant was shown 200 of these images (of the gender they declared being attracted to) while their brain activity was recorded using EEG. After viewing an image, the participant had to indicate whether they found the person in the picture attractive or not. In a subsequent experiment, the researchers told participants that their profile picture had been sent to other participants for feedback. They were then shown images of the people they had found attractive, accompanied by feedback indicating whether that person was supposedly attracted to them in return. This feedback, however, was fabricated. The deception was revealed to participants after the experiment concluded. After viewing this feedback, participants had to indicate whether they were happy with it or not (a question designed to ensure engagement). During this time, EEG recordings of their brain activity continued. In this way, the researchers simulated the core feedback loop of attraction and potential rejection common to most dating apps. The researchers tested multiple machine learning models to predict participants’ romantic emotions. The best-performing models achieved accuracy rates significantly above chance. Prediction accuracy was higher for romantic rejection (predicting the brain’s response to the feedback with 81.3% accuracy) than for initial attraction (71.3% accuracy). Results also showed that the machine learning models were more accurate when predicting the responses of picky participants (those who found fewer people attractive) than for those who were less fussy, suggesting that picky individuals may have stronger or more distinct neural signals related to romantic preference. “By analyzing EEG signals, we can predict user actions on dating apps, such as whether they will swipe right or left on a potential match. This prediction can offer insights into user emotions, including whether they find someone physically appealing or experience negative emotions related to rejection,” the study authors concluded. The study contributes to the development of technological ways to decode and interpret human brain responses. However, it should be noted that the study was carried out on a small group of students using static pictures of people. Real dating app profiles usually contain more information than just a picture. Additionally, people use dating apps when they actively try to connect with potential partners, something that might not have been the case with all students participating in this study. Studies of brain responses of individuals fully engaged in searching for a romantic partner might not yield identical results. The paper, “Can your brain signals reveal your romantic emotions?,” was authored by Dor Zazon and Nir Nissim.
Researchers predict romantic attraction using brain signals and AI #Science #ComputerScience #ArtificialIntelligence #BrainSignals #RomanticAttraction #AIResearch
https://purescience.news/article?id=959393
https://purescience.news/article?id=959393
Researchers predict romantic attraction using brain signals and AI
A small study in Israel developed a machine learning model that achieved moderate to high accuracy in predicting romantic attraction and rejection in a simulated dating app scenario. The researchers also found that the model was better able to predict the romantic emotions of picky participants than for those who were less fussy about their romantic interests, suggesting that picky individuals might exhibit stronger, more distinct brain activity related to romantic preferences. The paper was published in Computers in Biology and Medicine. Romantic love is an important aspect of human relationships. For many, it is the most important aspect of their lives. Romantic love promotes intimacy, bonding, and long-term pair connections that support both individual well-being and social stability. Across cultures, romantic love is linked to companionship, emotional security, and the motivation to form families. Psychologically, it engages brain systems tied to reward and attachment, which can foster resilience and life satisfaction. In modern societies, however, the ways people meet potential partners have changed significantly. Dating apps provide a new method for initiating relationships, expanding social networks beyond traditional circles of friends, workplaces, or communities. They can increase efficiency by matching people based on preferences, values, or location, and they offer opportunities to individuals who might otherwise struggle with limited social exposure. At the same time, dating apps can sometimes promote superficial judgments, encourage serial dating, or create unrealistic expectations. Study authors Dor Zazon and Nir Nissim wanted to explore whether machine learning can be used to accurately infer an individual’s romantic emotions based on the analysis of their neural responses to fictional dating profiles using electroencephalography (EEG). They aimed to predict attraction by analyzing event-related potentials (ERPs)—the brain’s specific electrical responses to stimuli like seeing a face—which were captured by the EEG. The 61 participants were students at Ben-Gurion University of the Negev in Israel, aged 23 to 32, and included 31 women. Study authors used 400 free images of people (200 men, 200 women) between 20 and 30 years of age downloaded from the internet to serve as visual stimuli. Each participant was shown 200 of these images (of the gender they declared being attracted to) while their brain activity was recorded using EEG. After viewing an image, the participant had to indicate whether they found the person in the picture attractive or not. In a subsequent experiment, the researchers told participants that their profile picture had been sent to other participants for feedback. They were then shown images of the people they had found attractive, accompanied by feedback indicating whether that person was supposedly attracted to them in return. This feedback, however, was fabricated. The deception was revealed to participants after the experiment concluded. After viewing this feedback, participants had to indicate whether they were happy with it or not (a question designed to ensure engagement). During this time, EEG recordings of their brain activity continued. In this way, the researchers simulated the core feedback loop of attraction and potential rejection common to most dating apps. The researchers tested multiple machine learning models to predict participants’ romantic emotions. The best-performing models achieved accuracy rates significantly above chance. Prediction accuracy was higher for romantic rejection (predicting the brain’s response to the feedback with 81.3% accuracy) than for initial attraction (71.3% accuracy). Results also showed that the machine learning models were more accurate when predicting the responses of picky participants (those who found fewer people attractive) than for those who were less fussy, suggesting that picky individuals may have stronger or more distinct neural signals related to romantic preference. “By analyzing EEG signals, we can predict user actions on dating apps, such as whether they will swipe right or left on a potential match. This prediction can offer insights into user emotions, including whether they find someone physically appealing or experience negative emotions related to rejection,” the study authors concluded. The study contributes to the development of technological ways to decode and interpret human brain responses. However, it should be noted that the study was carried out on a small group of students using static pictures of people. Real dating app profiles usually contain more information than just a picture. Additionally, people use dating apps when they actively try to connect with potential partners, something that might not have been the case with all students participating in this study. Studies of brain responses of individuals fully engaged in searching for a romantic partner might not yield identical results. The paper, “Can your brain signals reveal your romantic emotions?,” was authored by Dor Zazon and Nir Nissim.
Hacker News40 percent of MRI signals do not correspond to actual brain activity
#HackerNews #MRI #BrainActivity #Neuroscience #Research #MedicalImaging #BrainSignals
If a Meta AI model can read a brain-wide signal, why wouldn't the brain?
https://1393.xyz/writing/if-a-meta-ai-model-can-read-a-brain-wide-signal-why-wouldnt-the-brain
#HackerNews #MetaAI #BrainSignals #NeuralNetworks #CognitiveScience #AIResearch
The neuroTUM #StudentClub is exploring how #BrainComputerInterfaces can assist those with #PhysicalDisabilities. Their pilot, Leon, aims to control a computer using only #BrainSignals as they prepare for the upcoming #Cybathlon2024: http://go.tum.de/613738
📷A.Schmitz
AutisticMumTo3 She/herADHD linked to disrupted brain signals involved in focusing attention | New Scientist
https://www.newscientist.com/article/2385899-adhd-linked-to-disrupted-brain-signals-involved-in-focusing-attention/
#ADHD
#Brain
#BrainSignals
#PrefrontalCortex
#FrontalMidlineThetaBrainSignals
Phys.orgReferenced link: https://medicalxpress.com/news/2023-05-eyes-mouse-decoding-brain.html
Discuss on https://discu.eu/q/https://medicalxpress.com/news/2023-05-eyes-mouse-decoding-brain.html
Originally posted by Phys.org / @physorg_com: http://nitter.platypush.tech/medical_xpress/status/1653776450875473922#m
RT by @physorg_com: Seeing through the eyes of a mouse by decoding its #brainsignals @epfl_en @nature https://medicalxpress.com/news/2023-05-eyes-mouse-decoding-brain.html
Seeing through the eyes of a mouse by decoding its brain signals
Is it possible to fully reconstruct what someone sees based on brain signals alone? The answer is no, not yet. But EPFL researchers have made an important step in that direction by introducing a new algorithm for building artificial neural network models that capture brain dynamics with an impressive degree of accuracy.