Abstract: An artificial intelligence (AI) and machine learning (ML) based graphical user interface (GUI) system for early detection of depression symptoms using facial expression recognition and electroencephalogram is provided via receiving image data of a biological subject under examination for depression; receiving, from the biological subject, electroencephalogram data; analyzing the image data, via a first machine learning model, to identify an emotional state conveyed by a face of the biological subject; analyzing the electroencephalogram data, via a second machine learning model, to identify a severity level of depression in the biological subject; and providing the emotional state identified by the first machine learning model and the severity level identified by the second machine learning model to a graphical user interface.

Abstract: Artificial intelligence-assisted gait analysis of Christianson syndrome patients is provided via recording video of a subject performing a walking task; analyzing the video to determine poses and relationships between a plurality of points of the subject corresponding to anatomical features of the subject while performing the walking task; analyzing, using a machine learning model, a gait of the subject based on the relationships between the plurality of points while performing the walking task; classifying, using the machine learning model, the subject as exhibiting or not exhibiting symptoms of a motor disorder based on the gait; and outputting, via a graphical user interface, a determination of the subject as exhibiting or not exhibiting the symptoms of the motor disorder.

Abstract: A drug screening method uses electroencephalogram (EEG) or electromyogram (EMG) data applied to a ML model. EEG or EMG data is measured from a first animal species during administration of a seizure-inducing agent. A ML model is trained with the first animal species EEG or EMG data as well as measured EEG or EMG from a second animal species such that the trained ML model is able to identify a neurological adverse event in the second animal species based on data from the first animal species. A potential drug candidate is screened by administering the potential drug candidate to the first animal species and measuring the EEG or EMG data of the first animal species during the potential drug candidate administration. The measured EEG or EMG data is applied to the ML model to determine whether there is the neurological adverse event associated with the drug candidate administration.

Abstract: A neurostimulation system includes an electromyographic (EMG) electrode; a neural electrode implantable in a deep cerebellar nuclei of a subject; a data acquisition unit in communication with the EMG electrode for receiving and transmitting a EMG signal; and a processor in communication with the data acquisition unit, the processor generates a EMG pattern based on the EMG signal and outputs a stimulation signal to the neural electrode when the EMG pattern is indicative of an abnormal motor movement. A method of modulating an abnormal motor movement of a subject by using the neurostimulation system.

Abstract: A neurostimulation system includes an electromyographic (EMG) electrode; a neural electrode implantable in a deep cerebellar nuclei of a subject; a data acquisition unit in communication with the EMG electrode for receiving and transmitting a EMG signal; and a processor in communication with the data acquisition unit, the processor generates a EMG pattern based on the EMG signal and outputs a stimulation signal to the neural electrode when the EMG pattern is indicative of an abnormal motor movement. A method of modulating an abnormal motor movement of a subject by using said neurostimulation system.