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In the article intrinsic difference of information process in neural networks of the human brain and in artificial networks of the computer is considered. It is underlined, a brain of a person and computer, being the systems realizing formation of a new information by means of integration information already fixed in memory (“old” information), use for its production different forms of a determinism. In electric networks of the computer “works” classical physical determinism. Whereas in neural networks of a brain the forms of causality is “self determination” realized by means of mental processes. Structural and functional features of such determinism are described. 

This article is from Surgical Neurology International , volume 2 . Abstract Neurosurgery has always had a strong interest in innovating new technologies to improve neurological function and quality of life. Now, novel interventions that modulate central nervous system activity at the nanoparticle, molecular, genetic, cellular, and network level all seem to be on the horizon. Advances in biomedical engineering, including imaging techniques, sensor technologies, bio-signal analyses and classification, and prosthetics, have particularly accelerated the development brain-computer interfaces (BCI). Clinical translation of BCI technology will require multidisciplinary collaboration and effort to develop all necessary components, including advanced sensor technologies, sophisticated and real-time signal analyses and classifications, and complex effector technologies. Although the field has primarily been driven by basic scientists, neurosurgeons need to play a critical role in the further development of each component of these technologies because of our unique access to the awake and behaving human brain, our perspective with respect to the practicalities of technology implementation in the clinical setting, and because of our historical commitment to improving neurological function and quality-of-life. The current state of BCI research, the challenges, and the critical role that neurosurgeons must play in BCI development are briefly reviewed to advocate for increased neurosurgical involvement and commitment to this emerging translational field.

Event-related potentials (ERPs) are the summed membrane potentials of large numbers of simultaneously active neurons. To determine the anatomical location of the population of cells that produces a specific ERP, one must first record its responses from a number of points to characterize the spatial distribution of its surface field. A number of factors affect the validity of spatial analyses of ERP phenomena. These include the number of recording electrodes used, the choice of the reference electrode, any distortions introduced when preprocessing the EEG, and any distortions introduced by the mapping process itself. This report discusses a variety of methods that have been used to generate spatial representations of the electroencephalogram. It deals with pertinent aspects of the recording process, as well as with the spatial analysis per se. Some limitations of the various approaches are described, and a general plan of implementation adaptable to most situations is outlined. The Appendix contains two computer programs that can be used to generate surface-distribution maps of ERPs. Keywords: Brain mapping, Evoked potentials, Electroencephalography. (aw)

This unit consists of four sequences. The first considers the brain, the nervous system, and disorders of the brain. The second sequence deals with applications of the computer in diagnosis of brain disorders along with mathematical and statistical principles used in health applications. The third sequence is concerned with drugs and their effects on the nervous system. The final sequence deals with senses, primarily sight and hearing. The properties of light and sound of medical importance are considered. Supplementary lessons on concepts of electricity are provided. (Author/RE)

The purpose of this report is to test a commercially available portable computer and electrophysiologic data acquisition system under a variety of simulated environmental conditions. This durability test would establish the feasibility of this computer to collect human eletrophysiologic data in military subjects operating under extreme or adverse environmental conditions. A Dolch 486DX33 MHz 240 MB portable computer, a Bio-Logic Systems Corporation brain-mapping system card, an electrode attachment box, and a wave-form generator to simulate data acquisition and analysis were subjected to various environmental and physical conditions to establish operational specifications. Brain-mapping software was operating during all phases of testing. Norton Utilities Version 6.0, PC Tools Version 6.0 and Microsoft Version 6.21 Scandisk and Checkdisk Utilities were used to test for hard drive integrity and functionality. The computer's ability to operate and acquire synthetic biological data under various simulated field (Temperature and Relative Humidity (RH)) conditions was assessed in an Environmental Chamber. The computer operated for 1 h under each of the following conditions: Arctic (15 deg F/15% RH), Desert (120 deg F/15% RH), Jungle (105 deg F/85% RH). The computer was subjected to two simulated altitude flight profiles: a) high altitude flight, and b) rapid decompression (cabin pressure loss). The high altitude profile was 15,000 ft x 10 m, 20,000 ft x 10 m, 25,000 ft x 10 m 30,000 ft x 10 m, and 35,000 ft x 10 m. In the rapid decompression profile, the computer operating at 8000 ft, then rapidly depressurized to 22,000 ft over 2.07 s, and then returned to sea level at 36,000 ft per min. The computer was subjected to various acceleration profiles typical of aircraft flight (4.62-6.5-G forces), on a 20-foot centrifuge arm. The computer, hardware and software systemand acceleration/deceleration profiles tested. All component manufacturers' specifications were exceeded.

82 p. 21 cm

Combine two of our most powerful tools—the brain and the computer—and vast medical breakthroughs come into focus. A paralyzed man signals his limbs to move, machines translate thoughts into words, and sight is restored with a bionic eye. Implants that stimulate specific brain regions and devices that analyze electrical activity are revolutionizing treatments for epilepsy, spinal cord injury, stroke, and much more. But as immense amounts of neural data are collected, privacy experts, ethicists, and regulators are thinking not only about the importance of improving health but also about safeguarding “cognitive liberty.”

Combine two of our most powerful tools—the brain and the computer—and vast medical breakthroughs come into focus. A paralyzed man signals his limbs to move, machines translate thoughts into words, and sight is restored with a bionic eye. Implants that stimulate specific brain regions and devices that analyze electrical activity are revolutionizing treatments for epilepsy, spinal cord injury, stroke, and much more. But as immense amounts of neural data are collected, privacy experts, ethicists, and regulators are thinking not only about the importance of improving health but also about safeguarding “cognitive liberty.”

Designed to accompany the student text on the nervous system, this manual presents laboratory activities dealing with concepts presented in the text. Thirty-seven activities are described. Four supplementary activities dealing with concepts in electricity are also included. Laboratory activities are divided into several parts, each part covering a specific experiment dealing with the concept covered by the activity. Each part includes discriptions of materials, procedures, and discussion questions. (RE)

Designed to accompany the student text on the nervous system, this manual presents laboratory activities dealing with concepts presented in the text. Thirty-seven activities are described. Four supplementary activities dealing with concepts in electricity are also included. Laboratory activities are divided into several parts, each part covering a specific experiment dealing with the concept covered by the activity. Each part includes discriptions of materials, procedures, and discussion questions. (RE)

EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous or surface Laplacian transformed EEG signals. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used, Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). In this paper, recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements, Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and those obtained with the SL computed from 26 electrodes (full resolution SL). Recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%),

EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous or surface Laplacian transformed EEG signals. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used, Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). In this paper, recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements, Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and those obtained with the SL computed from 26 electrodes (full resolution SL). Recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%),

This article is from Frontiers in Neuroengineering , volume 7 . Abstract Individuals with autism spectrum disorder (ASD) show deficits in social and communicative skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. Evidence for and against the idea that dysfunctions in the mirror neuron system are involved in imitation and could be one underlying cause for ASD is discussed in this review. Neurofeedback interventions have reduced symptoms in children with ASD by self-regulation of brain rhythms. However, cortical deficiencies are not the only cause of these symptoms. Peripheral physiological activity, such as the heart rate and its variability, is closely linked to neurophysiological signals and associated with social engagement. Therefore, a combined approach targeting the interplay between brain, body, and behavior could be more effective. Brain–computer interface applications for combined neurofeedback and biofeedback treatment for children with ASD are currently nonexistent. To facilitate their use, we have designed an innovative game that includes social interactions and provides neural- and body-based feedback that corresponds directly to the underlying significance of the trained signals as well as to the behavior that is reinforced.

We have developed and tested two EEG-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KF LS classifier to map power spectra of 30-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject s average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: a) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal EOG signals, b) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from eight electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about three minutes. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 seconds for movement initiation and turning.

We have developed and tested two EEG-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KF LS classifier to map power spectra of 30-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject s average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: a) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal EOG signals, b) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from eight electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about three minutes. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 seconds for movement initiation and turning.

The development of increasingly sophisticated and powerful computers in the last few decades has frequently stimulated comparisons between them and the human brain. Such comparisons will become more earnest as computers are applied more and more to tasks formerly associated with essentially human activities and capabilities. The expectation of a coming generation of intelligent computers and robots with sensory, motor and even intellectual skills comparable in quality to our own is becoming more widespread and is leading to a new and potentially productive analytical science of information processing. In no field has this new approach been so precisely formulated and so thoroughly exemplified as in the field of vision. As the dominant sensory modality of man, vision is one of the major keys to our mastery of the environment, to our understanding and control of the objects wich surroud us. If we wish to create robots capable of performing complex manipulative tasks in a changing environment, we must surely endow them with (among other things) adequate visual powers. How can we set about designing such flexible and adaptive robots? In designing them, can we make use of our rapidly growing knowledge of the human brain, and if so, how at the same time, can our experience in designing artificial vision systems help us to understand how the brain analyzes visual information?

Brain-computer interfaces (BCIs) represent a revolutionary integration of neuroscience and engineering, enabling direct communication between the human brain and external devices. These interfaces have shown immense potential in applications ranging from medical rehabilitation and assistive technologies to neurogaming and cognitive enhancement. BCIs leverage neural signals to facilitate motor control, speech generation, and brain-state monitoring, utilizing invasive and non-invasive methods. However, significant challenges remain, including improving signal acquisition, enhancing processing algorithms, addressing ethical concerns, and ensuring data privacy. This paper examines the foundational principles of BCIs, their technological advancements, their applications in healthcare, and emerging ethical dilemmas. With rapid developments in machine learning, signal processing, and neural implants, BCIs are poised to become integral to both medical and consumer technologies, paving the way for enhanced human machine interaction. 

This volume contains the lesson plans and appropriate teacher background material for a 37-lesson sequence on the nervous system in health and medicine. Additional material is provided for supplementary lessons on concepts of electricity. Associated material, contained in separate volumes, include a student text and a student laboratory manual. Each lesson outline includes a rationale, objectives, sequence of student text lessons and laboratory activities, suggestions, teaching notes, materials (where appropriate), and anticipated results. (RE)

This volume contains the lesson plans and appropriate teacher background material for a 37-lesson sequence on the nervous system in health and medicine. Additional material is provided for supplementary lessons on concepts of electricity. Associated material, contained in separate volumes, include a student text and a student laboratory manual. Each lesson outline includes a rationale, objectives, sequence of student text lessons and laboratory activities, suggestions, teaching notes, materials (where appropriate), and anticipated results. (RE)

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This article is from PLoS ONE , volume 8 . Abstract Objective: Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. Approach: We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Main Results: Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Significance: Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research.

This article is from Frontiers in Neuroengineering , volume 7 . Abstract A quarter of century ago, Farwell and Donchin (1988) described their mental prosthesis for “talking off the top of your head.” This innovative communication system, later named P3-speller, has been the most investigated and tested brain–computer interface (BCI) system, to date. A main goal of the research on P3-spellers was the development of an effective assistive device for patients with severe motor diseases. Among these patients are those affected by amyotrophic lateral sclerosis (ALS). ALS patients have become a target population in P3-speller (and more generally in BCI) research. The P3-speller relies on the visual sensory modality, and it can be controlled by requiring users to actively move their eyes. Unfortunately, eye-movement control is usually not spared in the last stages of ALS, and, then, it is definitively lost in the case of complete paralysis. We reviewed the literature on ALS patients tested by means of P3-speller systems. Our aim was to investigate the evidence available to date of the P3-spellers effectiveness in ALS patients. To address this goal, a meta-analytic approach was adopted. The pooled classification accuracy performance, among retrieved studies, was about 74%. This estimation, however, was affected by significant heterogeneity and inconsistency among studies. This fact makes this percentage estimation (i.e., 74%) unreliable. Nowadays, the conclusion is that the initial hopes posed on P3-speller for ALS patients have not been met yet. In addition, no trials in which the P3-speller has been compared to current assistive technologies for communication (e.g., eye-trackers) are available. In conclusion, further studies are required to obtain a reliable index of P3-speller effectiveness in ALS. Furthermore, comparisons of P3-speller systems with the available assistive technologies are needed to assess the P3-speller usefulness with non-completely paralyzed ALS-patients.

This article is from Frontiers in Neuroengineering , volume 7 . Abstract A quarter of century ago, Farwell and Donchin (1988) described their mental prosthesis for “talking off the top of your head.” This innovative communication system, later named P3-speller, has been the most investigated and tested brain–computer interface (BCI) system, to date. A main goal of the research on P3-spellers was the development of an effective assistive device for patients with severe motor diseases. Among these patients are those affected by amyotrophic lateral sclerosis (ALS). ALS patients have become a target population in P3-speller (and more generally in BCI) research. The P3-speller relies on the visual sensory modality, and it can be controlled by requiring users to actively move their eyes. Unfortunately, eye-movement control is usually not spared in the last stages of ALS, and, then, it is definitively lost in the case of complete paralysis. We reviewed the literature on ALS patients tested by means of P3-speller systems. Our aim was to investigate the evidence available to date of the P3-spellers effectiveness in ALS patients. To address this goal, a meta-analytic approach was adopted. The pooled classification accuracy performance, among retrieved studies, was about 74%. This estimation, however, was affected by significant heterogeneity and inconsistency among studies. This fact makes this percentage estimation (i.e., 74%) unreliable. Nowadays, the conclusion is that the initial hopes posed on P3-speller for ALS patients have not been met yet. In addition, no trials in which the P3-speller has been compared to current assistive technologies for communication (e.g., eye-trackers) are available. In conclusion, further studies are required to obtain a reliable index of P3-speller effectiveness in ALS. Furthermore, comparisons of P3-speller systems with the available assistive technologies are needed to assess the P3-speller usefulness with non-completely paralyzed ALS-patients.

This article is from Brain Sciences , volume 3 . Abstract Gamma band oscillations in the human brain (around 40 Hz) play a functional role in information processing, and a real-time assessment of gamma band activity could be used to evaluate the functional relevance more directly. Therefore, we developed a source based Brain-Computer-Interface (BCI) with an online detection of gamma band activity in a selective brain region in the visual cortex. The BCI incorporates modules for online detection of various artifacts (including microsaccades) and the artifacts were continuously fed back to the volunteer. We examined the efficiency of the source-based BCI for Neurofeedback training of gamma- and alpha-band (8–12 Hz) oscillations and compared the specificity for the spatial and frequency domain. Our results demonstrated that volunteers learned to selectively switch between modulating alpha- or gamma-band oscillations and benefited from online artifact information. The analyses revealed a high level of accuracy with respect to frequency and topography for the gamma-band modulations. Thus, the developed BCI can be used to manipulate the fast oscillatory activity with a high level of specificity. These selective modulations can be used to assess the relevance of fast neural oscillations for information processing in a more direct way, i.e., by the adaptive presentation of stimuli within well-described brain states.

In this classic work, one of the greatest mathematicians of the twentieth century explores the analogies between computing machines and the living human brain. John von Neumann, whose many contributions to science, mathematics, and engineering include the basic organizational framework at the heart of today's computers, concludes that the brain operates both digitally and analogically, but also has its own peculiar statistical language. In his foreword to this new edition, Ray Kurzweil, a futurist famous in part for his own reflections on the relationship between technology and intelligence, places von Neumann’s work in a historical context and shows how it remains relevant today.