Adaptive User Interaction Systems For Interfacing With Cognitive Processes
A method for modifying cognitive processes includes receiving respective electroencephalogram (EEG) signals from EEG sensors, where the EEG signals are of a brain of a user. Features are extracted from the respective EEG signals. A cognitive state of the brain of the user is obtained from a first machine learning (ML) model that uses the features as input. Feedback parameters of a feedback signal are obtained from a second model that uses the cognitive state as input. The feedback signal is and provided to the user and using a user device according to the feedback parameters.
1 . A method for modifying cognitive processes, comprising:
receiving respective electroencephalogram (EEG) signals from EEG sensors, wherein the EEG signals are of a brain of a user;
extracting features from the respective EEG signals;
obtaining, from a first machine learning (ML) model that uses the features as input, a cognitive state of the brain of the user;
obtaining, from a second ML model that uses the cognitive state as input, feedback parameters of a feedback signal; and
providing, to the user and using a user device, the feedback signal according to the feedback parameters.
2 . The method of claim 1 , wherein the cognitive state of the brain of the user comprises a classification of whether the brain is focused or is wandering.
3 . The method of claim 1 , wherein the cognitive state of the brain of the user comprises a weighted exogenesis focus, a weighted endogenous focus, a weighted mind-wandering, a weighted concentration parameter, and a weighted stress parameter.
4 . The method of claim 1 , wherein extracting the features from the respective EEG signals comprises:
extracting the features from the respective EEG signals by a feature extractor wherein the feature extractor is separate from the first ML model.
5 . The method of claim 1 , wherein extracting the features from the respective EEG signals comprises:
extracting the features from the respective EEG signals by the first ML model.
6 . The method of claim 1 , wherein the second ML model further uses previous parameters of the feedback signal as input.
7 . The method of claim 1 , wherein the user device is a wrist-worn device and the feedback signal is a haptic feedback signal.
8 . The method of claim 1 , wherein the user device is a portable device that outputs audio and the feedback signal is an audio feedback signal.
9 . The method of claim 8 , wherein the feedback parameters comprise at least two of a pitch, tone, duration, and a delay of the audio feedback signal.
10 . A device for modifying cognitive processes, comprising:
a processor configured to:
receive respective electroencephalogram (EEG) signals from EEG sensors, wherein the EEG signals are of a brain of a user;
extract features from the respective EEG signals;
obtain, from a first machine learning (ML) model that uses the features as input, a cognitive state of the brain of the user;
obtain, from a second ML model that uses the cognitive state as input, feedback parameters of a feedback signal; and
provide, to the user, the feedback signal according to the feedback parameters.
11 . The device of claim 10 , wherein the cognitive state of the brain of the user comprises a classification of whether the brain is focused or is wandering.
12 . The device of claim 10 , wherein the cognitive state of the brain of the user comprises a weighted exogenesis focus, a weighted endogenous focus, a weighted mind-wandering, a weighted concentration parameter, and a weighted stress parameter.
13 . The device of claim 10 , wherein to extract the features from the respective EEG signals comprises to:
extract the features from the respective EEG signals by a feature extractor wherein the feature extractor is separate from the first ML model.
14 . The device of claim 10 , wherein to extract the features from the respective EEG signals comprises to:
extract the features from the respective EEG signals by the first ML model.
15 . The device of claim 10 , wherein the second ML model further uses previous parameters of the feedback signal as input.
16 . The device of claim 10 , wherein the device is a wrist-worn device and the feedback signal is a haptic feedback signal.
17 . The device of claim 10 , wherein the device is a portable device that outputs audio and the feedback signal is an audio feedback signal.
18 . The device of claim 17 , wherein the feedback parameters comprise at least two of a pitch, tone, duration, and a delay of the audio feedback signal.
19 . A system for adaptive adjustment of feedback signals, comprising:
an acquisition module configured to acquire EEG signals of a user;
an extraction module configured to extract features from the EEG signals;
a first ML module to obtain a cognitive state of a brain of the user;
a second ML module to obtain feedback parameters of a feedback signal based on the cognitive state of the brain of the user; and
a feedback module configured to provide the feedback signal to the user according to the feedback parameters.
20 . The system of claim 19 , wherein the cognitive state of the brain of the user comprises a weighted exogenesis focus, a weighted endogenous focus, a weighted mind-wandering, a weighted concentration parameter, and a weighted stress parameter.