An electronic device includes a housing including a first cover member, a second cover member, and a side member enclosing a space between the first cover member and the second cover member; a support member coupled to or formed integrally with the side member; a printed circuit board disposed in the space and including a biometric circuit; a first conductive portion disposed at least partially in the side member; a second conductive portion and third conductive portion disposed at least partially in the second cover member and electrically connected to the printed circuit board; and at least one conductive path disposed in the space, configured to electrically connect the biometric circuit and the first conductive portion, and formed on the support member. The biometric circuit receives a biometric signal based on the first conductive portion, the second conductive portion, the third conductive portion, and the at least one conductive path.

System for determining real-world effectiveness of various prescribed medications. Here a variety of different types of patient pulse wave measurements (e.g. blood pressure, pulse oximeter, ECG) and other physiological measurements are obtained. This actual data is compared to calculated measurements that would be expected based on the various patient baseline measurements in the absence of medication, schedule of medications, and impact of medications the various patient baseline measurements. If the actual data meets expectations, then the medication is likely acting as anticipated. Depending on which types of data do not meet expectations, problems with one or more previously described medications may be reported. Other types of patient physiological readings, such as temperature, motion, lung function, brain wave function (EEG) and the like may also be obtained, and these additional types of readings can be used to extend the range of different types of drugs/medications that the system can successfully monitor.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

A neurofeedback method, including: recording electrical signals from at least one brain region of a subject, wherein changes in the recorded electrical signals over time indicate changes in an activity level of the at least one brain region; providing an audio signal having a perceived quality based on the recorded electrical signals and according to an activity level of the at least one brain region; delivering the audio signal to the subject during said recording.

A system for performing functional brain mapping includes a memory configured to store first data from a magnetic resonance imaging (MRI) system and second data from electrodes. The system also includes a processor operatively coupled to the memory and configured to identify first edges in a brain network based on the first data from the MRI and second edges in the brain network based on the second data from the electrodes. The processor is configured to determine, based on the first edges and the second edges, connectivity metrics for the brain network. The processor is also configured to generate, based at least in part on the connectivity metrics, a decoder that differentiates between critical nodes and non-critical nodes in the brain network.

The disclosure is related configuring a vehicle according to a user profile, where an individual user profile can be selected from a registry containing one or more user profiles using electroencephalogram (EEG). An additional biometric, such as gait, can also be used. The user profiles can include a variety of the user preferences, such as the preferred climate, seat settings, mode of payment, route to take, and more. A mobile device can also be used to add a layer of security and privacy, where the mobile device has sole access to the user's user profile.

The disclosure is related configuring a vehicle according to a user profile, where an individual user profile can be selected from a registry containing one or more user profiles using electroencephalogram (EEG). An additional biometric, such as gait, can also be used. The user profiles can include a variety of the user preferences, such as the preferred climate, seat settings, mode of payment, route to take, and more. A mobile device can also be used to add a layer of security and privacy, where the mobile device has sole access to the user's user profile.

A method for detecting the presence of slowing patterns in an EEG sample comprising a plurality of channels of EEG signals, each channel comprising one or more segments, the method comprising: obtaining a first classifier that is trained to classify EEG samples as containing abnormal slow waves or not; performing a sequence of artifact removal processes on the EEG sample to generate a preprocessed EEG sample; extracting a first feature set from the preprocessed EEG sample; and passing the first feature set to the first classifier to predict whether the EEG sample contains abnormal slow waves or not; wherein the sequence of artifact removal processes comprises removal of one or more ocular artifacts and removal of one or more electrode artifacts.