Neuroadaptive Interface for Personalized Human-AI Interaction: A GAN-Enhanced Approach

Abstract

This invention introduces a novel framework for creating personalized and adaptive human-computer interfaces. By integrating neuroscience, artificial intelligence, and machine learning, this approach aims to enhance user experience and cognitive performance. A key component of this framework is the utilization of generative adversarial networks (GANs) to augment limited neurophysiological data. By training a GAN on real neurophysiological data, such as electroencephalogram (EEG) and electrodermal activity (EDA) signals, we can generate synthetic data that captures the underlying patterns and variations of human brain activity. This augmented dataset enables the training of robust machine learning models capable of accurately predicting user intent, cognitive load, and emotional state. The system comprises several key modules: a neurophysiological data acquisition module, a GAN-based data augmentation module, a feature extraction module, a machine learning model, and an interface adaptation module. The neurophysiological data acquisition module collects real-time neurophysiological data to capture user's cognitive and emotional states. The GAN-based data augmentation module generates synthetic neurophysiological data to expand the training dataset. The feature extraction module extracts relevant features from both real and synthetic data, such as brainwave patterns, heart rate variability, and skin conductance. The machine learning model, trained on the augmented dataset, classifies user states, predicts preferences, and estimates cognitive load. Finally, the interface adaptation module dynamically adjusts the interface's layout, color scheme, and interaction modalities based on real-time user data and model predictions. By continuously monitoring the user's neurophysiological state and adapting the interface accordingly, this system offers a promising solution for creating more intuitive, efficient, and personalized human-computer interactions.

Specification

Description:Field of the Invention
[001] This invention pertains to the field of human-computer interaction, artificial intelligence, and neuroscience. Specifically, it relates to a system and method for creating personalized and adaptive user interfaces that leverage neurophysiological data and generative adversarial networks (GANs) to enhance user experience and cognitive performance.
Description of Related Art
[002] Traditional human-computer interfaces often rely on static designs that do not adapt to individual user preferences and cognitive styles. This can lead to suboptimal user experiences, reduced efficiency, and increased cognitive load. Recent advancements in neuroscience and machine learning have enabled the development of neuroadaptive interfaces, which can dynamically adjust to user needs in real-time.
[003] However, a major challenge in developing neuroadaptive interfaces is the limited availability of high-quality neurophysiological data. Data augmentation techniques, such as random noise addition and data transformations, can help alleviate this issue. However, these methods may not effectively capture the complex patterns and variations inherent in human neurophysiological data.
[004] To address these limitations, this invention proposes a novel approach that utilizes GANs to generate synthetic neurophysiological data. By training a GAN on real neurophysiological data, we can create a diverse range of realistic data samples, which can be used to augment the training dataset and improve the performance of neuroadaptive models.
Summary of the Invention
[005] This invention presents a system and method for creating personalized neuroadaptive interfaces. The system comprises a neurophysiological data acquisition module, a GAN-based data augmentation module, a feature extraction module, a machine learning model, and an interface adaptation module.
[006] The neurophysiological data acquisition module collects real-time neurophysiological data, such as EEG and EDA signals, to capture user's cognitive state, emotional responses, and attention levels. The GAN-based data augmentation module generates synthetic neurophysiological data that is indistinguishable from real data. The feature extraction module extracts relevant features from the real and synthetic data, such as brainwave patterns, heart rate variability, and skin conductance. The machine learning model analyzes the extracted features to predict user preferences, cognitive load, and emotional state. The interface adaptation module uses the predicted information to dynamically adjust the interface's layout, color scheme, and interaction modalities to optimize user experience.
Detailed Description
[007] The GAN-based data augmentation module plays a crucial role in enhancing the performance of the neuroadaptive interface. By training a GAN on a limited amount of real neurophysiological data, we can generate a large number of synthetic data samples that capture the underlying patterns and variations. This augmented dataset can be used to train the machine learning model more effectively, leading to improved accuracy and generalization.
[008] The machine learning model, typically a deep learning model, can be trained to classify user states, predict user preferences, and estimate cognitive load. By continuously monitoring the user's neurophysiological signals, the interface can adapt to the user's changing needs in real-time.

, Claims:A system for creating a personalized neuroadaptive interface, comprising: a. A neurophysiological data acquisition module; b. A GAN-based data augmentation module; c. A feature extraction module; d. A machine learning model; and e. An interface adaptation module.
[2] The system of claim 1, wherein the GAN-based data augmentation module generates synthetic neurophysiological data.
[3] The system of claim 1, wherein the machine learning model is a deep neural network.
[4] A method for creating a personalized neuroadaptive interface, comprising: a. Acquiring neurophysiological data; b. Augmenting the neurophysiological data using a GAN; c. Extracting features from the augmented data; d. Training a machine learning model on the extracted features; and e. Adapting the interface based on the output of the machine learning model.
[5] The system of claim 1, wherein the neurophysiological data acquisition module includes at least one of the following: electroencephalography (EEG), electrodermal activity (EDA), eye-tracking, or electromyography (EMG) sensors.
[6] The system of claim 1, wherein the GAN-based data augmentation module utilizes a conditional GAN to generate synthetic data conditioned on specific user characteristics or task contexts.
[7] The system of claim 1, wherein the feature extraction module employs techniques such as time-frequency analysis, wavelet transforms, or deep learning-based feature extraction methods.
[8] The system of claim 1, wherein the machine learning model is a recurrent neural network (RNN), a long short-term memory (LSTM) network, or a convolutional neural network (CNN).
[9] The system of claim 1, wherein the interface adaptation module dynamically adjusts at least one of the following: layout, color scheme, font size, audio feedback, or haptic feedback.
[10] A method for creating a personalized neuroadaptive interface, comprising.
a. Acquiring real-time neurophysiological data from a user.
b. Augmenting the real-time neurophysiological data with synthetic data generated by a GAN.
c. Extracting features from the augmented neurophysiological data.
d. Training a machine learning model on the extracted features.
e. Predicting user intent, cognitive load, and emotional state using the trained machine learning model and
f. Adapting the interface based on the predicted user state.

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