The Brain-Computer Interface (BCI) and AI-powered mobile app leverages EEG signals to manage stress, attention, and cognitive load, ultimately enhancing overall learning outcomes.
The app should be designed to adapt to any learning subject, functioning independently of specific databases or content, allowing users to study various topics, from school subjects to personal development.
The app should use an AI-powered chatbot to facilitate interactive dialogue, identifying the user’s current study topic. It should determine key concepts, terms, and other educational components related to the topic. The app should assess comprehension, recall, and application of knowledge to accurately measure the depth of understanding.
The app should use EEG signals to detect activity in the hippocampus and prefrontal cortex, indicating confident recall of information. This enables the app to recognize when users accurately remember facts and concepts, providing appropriate feedback.
The app should be capable of identifying when users are guessing by detecting activation in the ventrolateral prefrontal cortex or temporal cortex, which signals uncertainty or false memories. This enables the app to evaluate user responses and provide supplementary questions to gauge knowledge acquisition.
The app should use trained GPT models to evaluate the accuracy of user responses. The app should cross-check answers, rephrase questions, or provide alternative queries to accurately evaluate the accuracy of their knowledge.
The app should use EEG signals, specifically tracking beta waves (12-23 Hz) in the frontal cortex, to monitor attention levels. If beta wave activity drops (indicating reduced focus), it should notify users to take breaks or adjust their learning pace, maintaining focus and engagement.
The app should analyze EEG signals, focusing on the balance between alpha waves (8-12 Hz) in the parietal and occipital regions and theta waves (4-8 Hz) in the limbic system. When there’s a decrease in alpha activity and an increase in theta activity (indicating rising stress), it should suggest relaxation techniques or breaks, helping users maintain a productive state.
The app should monitor EEG signals by tracking increased gamma wave activity (40-100 Hz) in the frontal cortex, indicating cognitive load. When gamma wave intensity rises beyond normal learning levels, it should suggest breaking complex topics into smaller parts or taking pauses to prevent burnout and improve information absorption.
This feature utilizes EEG signals to continuously monitor the user's brain activity and interpret them for subsequent use by other modules.
This module employs an AI-powered chatbot to facilitate guided dialogue with users, identifying learning topics, key concepts, and comprehension levels.
This module tracks and analyzes EEG signals, identifying user states such as concentration, stress, and cognitive load.
This feature identifies confident responses by tracking changes in the wave frequency of hippocampal and prefrontal cortex activity.
By detecting activity in the ventrolateral prefrontal cortex or temporal cortex, this feature identifies instances of guessing or uncertainty.
This module cross-references user responses with a GPT-powered knowledge base to identify incorrect answers and provide relevant recommendations.
This feature offers personalized recommendations and notes to help users identify and address their weaknesses, ultimately leading to mastery.
These features autonomously track EEG-based indicators of attention (beta waves), stress (alpha and theta waves), and cognitive load (gamma waves).
The app provides users with a unique tool that leverages their biological responses for real-time feedback on their learning process. By analyzing EEG signals, it offers precise insights on concentration, cognitive load, and stress levels.
Users benefit from targeted recommendations and personalized note-taking features that focus on their identified weaknesses. This feature ensures users can revisit challenging topics and strengthen their knowledge retention over time, thereby improving overall learning outcomes.
The app’s autonomous functions, such as stress, attention, and cognitive load management, serve as ongoing assistants. These features support users beyond educational contexts, making the app useful for enhancing productivity in various activities, especially those requiring focus and mental resilience.
The app’s adaptive, BCI-integrated learning experience boosts user engagement by providing personalized, science-backed guidance. Its cost-effective and scalable design supports expansion into various educational sectors, from schools to corporate training, enhancing product appeal and driving wider market adoption.
SEVEN’s team, processes and technical knowhow are unmatched in my experience. However, I would rank their commitment to deliver as their strongest attribute. SEVEN again and again has demonstrated an unbelievable level of commitment to supporting our promise to our customers.
Their work has been extremely solid. They’ve developed something that’s unique, intuitive, and aesthetically pleasing. They facilitate the exploration of my whims, but only when it’s feasible or sensible.
We have been collaborating with SEVEN since 2019, and the partnership has proven to be extremely fruitful. The project was executed smoothly and swiftly. SEVEN completed the task within the agreed timeframe, and the applications are now available on the App Store and Google Play.