The human brain acts as an intelligent sensor by helping in effective signal communication and execution of logical functions and instructions, thus, coordinating all functions of the human body. More importantly, it shows the potential to combine prior knowledge with adaptive learning, thus ensuring constant improvement. These qualities help the brain to interact efficiently with both, the body (brain-body) as well as the environment (brain-environment). This dissertation attempts to apply the brain-body-environment interactions (BBEI) to elevate human existence and enhance our day-to-day experiences. For instance, when one stepped out of the house in the past, one had to carry keys (for unlocking), money (for purchasing), and a phone (for communication). With the advent of smartphones, this scenario changed completely and today, it is often enough to carry just one's smartphone because all the above activities can be performed with a single device. In the future, with advanced research and progress in BBEI interactions, one will be able to perform many activities by dictating it in one's mind without any physical involvement. This dissertation aims to shift the paradigm of existing brain-computer-interfaces from just ‘control' to ‘monitor, control, enhance, and restore' in three main areas - healthcare, transportation safety, and cryptography. In healthcare, measures were developed for understanding brain-body interactions by correlating cerebral autoregulation with brain signals. The variation in estimated blood flow of brain (obtained through EEG) was detected with evoked change in blood pressure, thus, enabling EEG metrics to be used as a first hand screening tool to check impaired cerebral autoregulation. To enhance road safety, distracted drivers' behavior in various multitasking scenarios while driving was identified by significant changes in the time-frequency spectrum of the EEG signals. A distraction metric was calculated to rank the severity of a distraction task that can be used as an intuitive measure for distraction in people - analogous to the Richter scale for earthquakes. In cryptography, brain-environment interactions (BBEI) were qualitatively and quantitatively modeled to obtain cancelable biometrics and cryptographic keys using brain signals. Two different datasets were used to analyze the key generation process and it was observed that neurokeys established for every subject-task combination were unique, consistent, and can be revoked and re-issued in case of a breach. This dissertation envisions a future where humans and technology are intuitively connected by a seamless flow of information through ‘the most intelligent sensor', the brain.

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Tato práce je shrnutím existujících přístupů pro zpracování EEG signálu za účelem EEG biofeedbacku a dále popisuje návrh a implementaci vlastní aplikace pro EEG biofeedback se zaměřením na trénink pozornosti. Dále obsahuje případovou studii provedenou na neurotypickém studentovi a studentovi s ADHD, která zkoumá vliv implementované aplikace na měřený EEG signál subjektů.

This work deals with problems of EEG biofeedback and possibilities of its use in therapy. The method of EEG biofeedback helps patients to achieve the state of relaxation. It is a noninvasive treatment modality. The paper also discussed the requirements for each component unit. The main point of a design and construction of Alpha activity monitor, a device implementing EEG biofeedback focused on sensing electrical activity of the brain, specifically alpha waves. Alpha activity is specific and their parameters are taken into account when selecting components and construction equipment. Custom feedback is then for the appearance of alpha activity mediated by the headphones in acoustic form. The work also includes design of electrical circuits and electrical diagram of the overall apparatus including a list of used parts. Electrical diagrams are formed in the EAGLE 6.1.0.

Several lines of evidence suggest that asymmetric anterior brain activation is related to affective style, linking left hemisphere activation to positive affect and right hemisphere activation to negative affect. However, previous reports of left frontal hypoactivation in depressed patients were not confirmed in recent studies. This study evaluated additional characteristics of resting EEG alpha (8–13 Hz) asymmetry in 15 clinically depressed patients and 22 healthy adults by recording EEG activity on two separate occasions, 2–4 weeks apart. Across both sessions, group differences in anterior EEG asymmetry were compatible with the original hypothesis. However, groups differed in temporal stability of anterior EEG asymmetry, which was retest reliable in controls but not depressed patients. In contrast, temporal stability of posterior EEG asymmetry was acceptable in both groups. Increased variability of anterior EEG asymmetry may be a characteristic feature for depression, and, if so, this would challenge the notion that anterior EEG alpha asymmetry is a trait marker for depression. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

The e-OPRA Implant System (Integrum AB, Sweden) is a system which employs permanently accessible implantable neuromuscular electrodes in combination with osseointegrated attachment of the prosthesis to the skeleton, in order to create a more natural control of advanced robotic upper-limb prostheses. The system enables the possibility of sensory feedback, via a cuff electrode to the ulnar nerve which allows for direct neurostimulation of the nerve. This work proposes a method using electroencephalography (EEG) to quantitatively evaluate the cognitive workload of a person controlling a prosthesis, and how said workload changes when sensory feedback is enabled. Based on previous studies on EEG and cognitive workload, the proposed methods include collecting EEG data from subjects who are performing a grasping task while listening to a selection of sounds and counting the number of times a specific tone is presented. The data is analysed using both event related potentials (ERPs) as well as spectral analysis. The method was used in a trial run consisting of two healthy subjects, and one transhumeral amputee implanted with the e-OPRA system. Although the subject group was not large enough to draw any statistical conclusions, the trial run and the results from it suggest that the methods could be used in a larger study to evaluate the cognitive workload of amputees implanted with the e-OPRA system.

Traditional antidepressants, which act on the serotonin, dopamine, and norepinephrine systems, require many weeks to produce a therapeutic effect and are not effective for every patient. A sub-anesthetic dose of the anesthetic agent ketamine, a glutamate N-methyl-D-aspartate receptor antagonist, has been shown to produce a rapid and robust antidepressant effect in treatment-resistant major depressive disorder (MDD). As depressive symptoms typically return after one week following a single infusion, recent work has begun to focus on methods for prolonging the effects. Repeated infusions on a specific dosing schedule are being explored, however, the early identification of treatment responders and non-responders would be beneficial for optimized treatment selection within this population. The mechanisms underlying ketamine’s rapid effects conceivably involve the regulation of altered glutamatergic signaling in MDD, though this is not yet completely understood. Understanding of the central mechanisms mediating ketamine’s rapid antidepressant effects may be increased through the use of non-invasive electroencephalographic measures, including resting electroencephalography (EEG) and the mismatch negativity (MMN) event-related potential. These measures have been shown to be altered in depressed individuals and are sensitive to ketamine administration. The primary objectives of this study were to 1) examine acute changes in EEG- and MMN-derived indices, immediately post- and two hours postinfusion, with a sub-anesthetic ketamine dose in comparison to an active placebo (midazolam), and 2) to examine their relationships with early and sustained antidepressant treatment response to ketamine within an eight week clinical trial involving three study phases. Ketamine decreased measures of scalp-level alpha and theta resting activity, immediately postinfusion, and increased gamma immediately and two hours postinfusion. An increase in source-localized anterior cingulate activity two hours postinfusion was also observed. Regarding the MMN, ketamine reduced frontal amplitudes as well as theta event-related oscillations and source-localized peak frontal generator activity. Measures of resting theta and change in gamma, as well as left frontal MMN amplitude, theta event-related oscillations, baseline left phase locking factor, and baseline right inferior temporal lobe activity were predictive of decreases in depressive symptoms at both early and sustained treatment time points. Alpha power was predictive of decrease in suicidal ideation, though the relationship with baseline and early change in symptoms was stronger. These findings contribute to our understanding of the role of baseline and ketamine-induced changes in both resting and task-evoked electrophysiological measures, and may have the potential to act as non-invasive biomarkers of antidepressant response prediction to glutamatergic agents.

The measurement of electrical activity at the scalp using EEG can provide great insight into cognition and information processing. For example, event-related potentials (ERPs) are positive or negative deflections that correspond to a stimulus or event. These ERPs can reflect error processing and attentional processes associated with a stimulus. Specifically, error-related negativity and feedback-related negativity (ERN, FRN), are related to performance/conflict monitoring. Furthermore, the P300 ERP reflects attentional processes in response to target stimuli. Previous research examining the P300 component on a complex learning task has demonstrated increased P300 amplitude in response to violations of participants’ expectations about task events. The current study extends this research by examining ERN and FRN amplitudes on trials with incorrect behavioral responses throughout the same learning task. Pilot data has been collected from four participants. Participants wore an electrode cap with 32 electrodes to record EEG data while completing a paired associate task. Pilot data has demonstrated an increased ERN amplitude 50ms after error commission on incorrect trials. The ERN and FRN amplitudes were greater for incorrect trials than for correct trials. Larger P300 amplitudes were also observed for the incorrect trials than for the correct trials. This extension upon previous findings provides further insight into the role of performance monitoring and error processing in learning.

Working memory (WM) is a crucial component of cognitive function that affects learning, reasoning, and problem solving, all of which are important for daily functioning. Therefore, addressing factors that can impact working memory, such as stress, are incredibly relevant to understanding WM efficiency. WM is an important component of higher order cognitive function and high WM capacity has been shown to be important for academic and occupational performance. Thus, understanding the relationship between stress-related factors and WM could aid in identifying strategies to mitigate the deleterious effects of stress on working memory. Although some previous research has indicated a negative impact of acute stress on WM, other research has indicated no impact or even a positive impact of stress on WM. As the relationship between acute stress and WM is mixed, examining other stress-related factors may provide further insight into the relationship. The current study examines how adverse childhood experiences (ACEs) and acute stress influence WM, and how frontal theta and alpha activity are affected by WM task demands. Participants completed a working memory task while their EEG was recorded. Participants then completed the PANAS to assess their current emotional state. Following the PANAS, participants viewed a stressful or neutral video as an acute stress induction, followed by a second PANAS to ensure effectiveness of stress induction. Participants then completed the WM task a second time. Finally, the participants completed the ACEs questionnaire. Bayesian linear mixed effects models were used to examine the relationships between ACEs, acute stress, WM, and frontal theta and alpha frequencies. Findings suggest there is not enough evidence to support a relationship between acute stress, ACEs, WM, and WM-related theta and alpha. While the current study did not reveal a relationship, future research should explore how acute stress and exposure to specific stressors during childhood could explain individual differences in WM.

Maintaining wakefulness is associated with a progressive increase in the need for sleep. This phenomenon has been linked to changes in synaptic function. The synaptic adhesion molecule Neuroligin-1 (NLG1) controls the activity and synaptic localization of N-methyl-D-aspartate receptors, which activity is impaired by prolonged wakefulness. We here highlight that this pathway may underlie both the adverse effects of sleep loss on cognition and the subsequent changes in cortical synchrony. We found that the expression of specific Nlg1 transcript variants is changed by sleep deprivation in three mouse strains. These observations were associated with strain-specific changes in synaptic NLG1 protein content. Importantly, we showed that Nlg1 knockout mice are not able to sustain wakefulness and spend more time in nonrapid eye movement sleep than wild-type mice. These changes occurred with modifications in waking quality as exemplified by low theta/alpha activity during wakefulness and poor preference for social novelty, as well as altered delta synchrony during sleep. Finally, we identified a transcriptional pathway that could underlie the sleep/wake-dependent changes in Nlg1 expression and that involves clock transcription factors. We thus suggest that NLG1 is an element that contributes to the coupling of neuronal activity to sleep/wake regulation.