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![The brain-computer interface proposed by J. Vidal [8].](profile/Marcin-Kolodziej-2/publication/258509662/figure/fig3/AS:297635341717509@1447973203454/The-brain-computer-interface-proposed-by-J-Vidal-8.png)
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![Fig. 3. The brain-computer interface proposed by J. Vidal [8].](profile/Marcin-Kolodziej-2/publication/258509662/figure/fig3/AS:297635341717509@1447973203454/The-brain-computer-interface-proposed-by-J-Vidal-8_Q320.jpg)
In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involv...
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Context 1
... already mentioned, the possibility of human-computer communication, realized solely by means of signals coming directly from the brain, was suggested almost 40 years ago! A brain-computer interface proposed by J. Vidal in 1973 is illustrated in Fig. 3. However, only in the last decade of the twentieth century, several research centers worldwide made bold attempts to use electroencephalography (EEG) for direct communication between the brain and the computer. ...
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Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography signals are distorted by movement artifacts and electromyography signals in ambulatory condition, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-...
Introduction
Language impairments often result from severe neurological disorders, driving the development of neural prosthetics utilizing electrophysiological signals to restore comprehensible language. Previous decoding efforts primarily focused on signals from the cerebral cortex, neglecting subcortical brain structures’ potential contributions...
Objective:
Dim target detection in remote sensing images is a significant and challenging problem. In this work, we seek to explore event-related brain responses of dim target detection tasks and extend the brain-computer interface (BCI) systems to this task for efficiency enhancement.
Methods:
We develop a BCI paradigm named Asynchronous Visual...
![Fig. 3. The brain-computer interface proposed by J. Vidal [8].](profile/Remigiusz-Rak/publication/265942691/figure/fig3/AS:669503306334215@1536633435719/The-brain-computer-interface-proposed-by-J-Vidal-8_Q320.jpg)
In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involv...
Electroencephalographs record the electrical activity of your brain through the scalp. Electroencephalography is difficult to obtain due to its sensitivity and variability. Applications of electroencephalography such as for diagnosis, education, brain-computer interfaces require large samples of electroencephalography recording, however, it is ofte...
Citations
... The mode of detection was classified into three types Invasive, Semi Invasive, and Non-Invasive Invasive method: Initially, the experiment of BCI with the invasive method was conducted on animals like mice, monkeys, and cats. These invasive methods require a surgical intervention which is used to cut the skin of the head or open the skull to place the electrode on the surface of the cortex [14] resulting in electrocorticography(ECoG) ECoG does not damage the neurons where the electrodes are not entered inside the brain. The main features of this invasive method are a good signal, Amplitude level with low noise, and spatial revolution will be good, each activity of the brain's neurons will be registered through the internal electrodes placed on the surface of the cortex This invasive method has a flaw which leads to a traditional fMRI procedures typically follow. ...
... Concurrently, the current state of BCI technology is investigated, including hardware, software, and signal processing algorithms. This examination provides an analysis of the operational principles and prevalent platforms of brain-computer interfaces [9]. Additionally, current trends in BCI research in educational, medical, and other domains are discussed in the literature, along with potential future applications and significant obstacles that must be overcome prior to widespread adoption. ...
In the evolution of intelligent technologies, the intersection of sophisticated interfaces and home automation has created unprecedented prospects for effortless and intuitive exchanges within our personal living environments. The objective of this study is to examine the amalgamation of Brain-Computer Interface (BCI) technology and home automation systems to determine whether direct neural interactions have the capacity to transform the way users interact with smart devices within their residences. The investigation challenges the traditional demarcation between human cognition and device manipulation, positing a future in which individuals may utilize cognitive signals to control devices. By examining the potential, challenges, and paradigm-shifting effects of BCI in home automation, this paper attempts to make a scholarly contribution to the ongoing dialogue surrounding the convergence of smart home technology and neuroscience. By examining unorthodox applications such as brain-computer interfaces (CCIs) in education, trends in freelancing, interactive learning, and IoT-enabled smart aquariums, this research investigates the revolutionary potential of BCI to enhance interaction and control in smart home environments. By integrating neuroscience, technology, and home automation, the interdisciplinary approach envisions a future in which inhabitants respond autonomously to cognitive signals and commands.
... The mode of detection was classified into three types Invasive, Semi Invasive, and Non-Invasive Invasive method: Initially, the experiment of BCI with the invasive method was conducted on animals like mice, monkeys, and cats. These invasive methods require a surgical intervention which is used to cut the skin of the head or open the skull to place the electrode on the surface of the cortex [14] resulting in electrocorticography(ECoG) ECoG does not damage the neurons where the electrodes are not entered inside the brain. The main features of this invasive method are a good signal, Amplitude level with low noise, and spatial revolution will be good, each activity of the brain's neurons will be registered through the internal electrodes placed on the surface of the cortex This invasive method has a flaw which leads to a traditional fMRI procedures typically follow. ...
. Brain-computer interface (BCI) acts as an important domain to determine human brain activities. Human Brain activities can be determined through various interfaces or electronic gadgets. BCI acts as an interface between the human brain and the computer. This paper describes various terminologies of wavelength used to determine human activities. It also describes the mode of detection of BCI such as MEG, fMRI, NIRS, and EEG with their classification, methodologies, and components. This Paper provides an exploration of various approaches and adaptations of the human brain's activities with various EEG techniques.
... Following this, Prof. Vidal of California University, developed BCI to achieve a technology to read the signals of the brain [11]. It wasn't until 1977 that Aranibar and Pfurtscheller conducted experiments to demonstrate that moving or envisioning moving specific body parts could cause changes in the frequency spectrum of EEG alpha (8)(9)(10)(11)(12) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) signals in the motor brain region. These changes would take place both at the start of the movement and throughout the process of migration. ...
... In the case of EEG analysis, the DFT can be used to identify specific frequency bands that are related to various mental conditions and processes. For example, alpha waves with a frequency band (8)(9)(10)(11)(12) are associated with relaxation and meditation, while beta waves with a frequency band (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) are associated with alertness and cognitive processing. Overall, the use of digital signal processing techniques like the DFT has revolutionized the field of EEG analysis and has led to significant advances in our understanding of the brain and its function. ...
... Step 1 Different pre-processing and signal-processing algorithms are used [23]. ...
The BCI (Brain-computer interface) is a new-age tool in which human or artificial body parts like prosthetic arms can be controlled by sensing the EEG signals. To understand the mental state of the brain, the study of EEG is very important but this technique has its own limitation, which has been identified in this work. Various signal-processing techniques of EEG have been studied to analyze the mental state of the brain. A novel cross-technique will be introduced to improve EEG signal processing techniques. The spatial and temporal resolution trade-off problem is the biggest challenge in EEG signal acquisition systems. This is resolved by taking the fMRI signal and then transforming it into an EEG signal by decomposition and comparing modules for better spatial resolution in the EEG signal. The main limitations like frequency overlapping and decomposition, of the feature extraction technique, have been investigated and an improvised general algorithm will be introduced in this proposed work. This work also touches briefly on different classification algorithms which follow different learning criteria and optimized techniques will be used to achieve better performance. The main objective of this proposed work is the identification of the best-suited algorithm for each step of signal processing to understand the mental state of the brain, brain oscillation characteristics, and some old and new techniques trends companion for the analysis of signal processing techniques of the brain.
... Tere are three forms of BCI invasiveness based on the position of electrodes [32][33][34]: (1) invasive BCIs that are embedded into the brain; (2) partial invasive BCIs that have the device implanted inside the skull but outside the brain; and (3) non-invasive BCI systems uses neuron imaging outside the skull. Invasive BCIs involve the implanting of microelectrodes that are implanted in the brain [4,35]. ...
... Historically, the focus of early BCI systems was to provide alternative output pathways for severely disabled individuals that would enable them to control external systems [33,45]. However, one of the main challenges for researchers in BCI system application is the training of individuals to use control mechanisms, and there are related to habituation and response rates, the actual time taken to train the user and fatigue, the need to modulate each device to the needs of the individual, lack of predictive indicators of performance due to individual circumstances, limited applicability, the ability to control the desired task with diferences in system activity, and the self-pace requirements of the individual [66][67][68]. ...
... Tere is a district clustering in the 2018 onward diagram that is indicative of the focus center of research and is centered on "human," "user," and terms such as "cognitive" "process," "technology," and "response," terms associated with the application of BCI [29,40,44,97] (Figure 6). Early reviews focused on the methods and achieved greater performance with words such as "access pathways" [66], "alternative communication" [67], "biocompatibility" [43], "classifcation" [57], "cognitive tasks" [31], "control" [33], "motor imagery" [7,36,70,79], "neuroengineering" [19,43] "neuroimaging" [37], and "sensory-motor regions" [79], all of which show a focus on improving the understanding of brain function and how BCI systems are able to exploit this knowledge. ...
This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of “normal” individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.
... The outcomes obtained from evoked potentials are being averaged with a presentation of repeated stimuli as the amplitude of potentials measured is small. The well-known evoked signals are Steady-State Visual Evoked Potentials (SSVEP) and P300 potentials and commonly used stimuli are visual (e.g., a flash of light), auditory (sound related), and sensory (Rak et al. 2012). Development of BCI relies upon the selection of signals, data acquisition methods, and feature extraction methods, development of training strategies, protocols, and choice of application and user group. ...
Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain–Computer Interface (BCI) is a tool that provides direct human–computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.
... In [20], sparse-matrix analysis was used to diagnose diseases and disturbances in the EEG signal. In [21], the brain-computer interface as measurement and control system is presented. This article surveyed several measurement and control systems, static as well as dynamic ones, for brain-computer interfaces that use EEG signals. ...
The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.
... ECoG are implanted under the skull either above the Dura Matter (epidural ECoG) or below (subdural ECoG). ECoG can be considered as "semi-invasive" recordings as the patient underwent a craniotomy but the brain integrity is not affected by the operation [Lebedev and Nicolelis, 2017] [Rak et al., 2012]. Due to the dimensions of the electrodes and the distance between the electrodes and the neurons, ECoG is limited to the neural population recording of the superficial layers of the cortex. ...
... is a non-invasive recording device based on a helmet/headset with a large number of electrodes (64 to 256) placed on the surface of the scalp. EEG signals integrate the extracellular currents of a large neural population over a large region (10 cm 2 or more) [Buzsáki et al., 2012] [Rak et al., 2012] [Waldert et al., 2009]. Similarly to ECoG recordings, EEG is limited to the recording of the low-pass filtered synchronous extracellular current activity of neurons at the surface of the motor cortex. ...
... The noninvasiveness, ease of use and low cost of EEG recording systems tend to apply it for research with a humongous number of studies in the BCI and the motor BCI field in the past years [Lebedev and Nicolelis, 2017] [Lotte et al., 2018]. Additionally, EEG recording was used for epileptic, sleep or brain disorder detection [Rak et al., 2012]. Nevertheless, EEG-based BCIs present several limitations compared to more invasive neural recording systems. ...
Brain-computer interfaces (BCIs) are systems that allow the control of external devices from the brain’s neural signals without neuromuscular activation. Among the various applications, functional compensation and rehabilitation of individuals suffering from severe motor disabilities (with motor BCIs) has always been a focus for BCI research. Brain signals are translated, through signal processing steps, into orders realized by an effector which returns feedbacks (visual, tactile, proprioceptive…) to the patient giving him back some mobility and autonomy. Nevertheless, numerous challenges to translate BCI from offline experiments based on healthy subjects recordings to daily life applications for disable patients. Even though BCI decoding highlights good control performance during specific task such as center out experiments, the development of decoder for online asynchronous decoding, stable during long period, is still one of the BCI community claim. Moreover, a lack of studies and algorithms on multi-limb effector control were highlighted.Relying on the “BCI and Tetraplegia” clinical trial of CEA/LETI/CLINATEC, the development of new decoders for real-time closed-loop adaptive asynchronous multi-limb is addressed in the present doctoral thesis. Recursive exponentially weighted Markov switching multi-linear model (REW-MSLM) was designed to handle complex / high dimensional multi-limb effector control with online closed-loop calibration of the decoding model.Based on a mixture of expert architecture, REW-MSLM allows a tetraplegic patient who underwent bilateral epidural electrocorticographic (ECoG) arrays implantation of chronic wireless implants (WIMAGINE) 8D control of a whole body exoskeleton over several months without model recalibration. The patient was able to perform alternative 3D left and right hand translations and 1D left and right wrist rotations with high accuracy and during long period without any model recalibration. Experiments with higher controlled dimensions and other effectors such as wheelchair have also been tested and highlighted promising results. This PhD thesis aims to present new innovative adaptive BCI decoder adapted to multi-limb decoding for clinical applications and highlights the interest of such decoder in the perspective of the current state-of-the-art.
... In these systems, users' intentions could be read by decoding the features of brain signals. The signals could be translated into specific commands that control devices such as computers and wheelchairs [1]. Measuring brain activities is the centerpiece in a BCI system. ...
Brain signals could be used to control devices to assist individuals with disabilities. Signals such as electroencephalograms are complicated and hard to interpret. A set of signals are collected and should be classified to identify the intention of the subject. Different approaches have tried to reduce the number of channels before sending them to a classifier. We are proposing a deep learning-based method for selecting an informative subset of channels that produce high classification accuracy. The proposed network could be trained for an individual subject for the selection of an appropriate set of channels. Reduction of the number of channels could reduce the complexity of brain-computer-interface devices. Our method could find a subset of channels. The accuracy of our approach is comparable with a model trained on all channels. Hence, our model's temporal and power costs are low, while its accuracy is kept high.
... A very popular and also quite effective brain-computer interfaces (BCI) [1][2][3][4][5][6] standard is based on the so-called Steady-State Visually Evoked Potentials (SSVEP) [7][8][9][10][11][12]. It uses the response of the brain to flickering light stimuli with a constant frequency. ...
Background: A growing number of neuroimaging studies have revealed spatial abnormalities of resting-state functional brain network activity in bipolar disorder (BD). Conversely, abnormalities of resting state temporal dynamics have been scarcely investigated so far. The aim of this study was to characterize the EEG microstates activity in BD patients with a history of manic predominant polarity. Patients were euthymic and pharmacologically stabilized.
Methods: Nineteen BD patients (mean age 34.4 ± 11.0, 7 female) and 19 healthy controls (HC; mean age 38.2 ± 9.9, 7 female) were recruited. The psychometric evaluation included the Hamilton Depression Scale (HAMD), the Young Mania Rating Scale (YMRS), the Dissociative Experience Scale (DES), and the State-Trait Anxiety Inventory (STAI). Two runs of 2 minutes of EEG activity by a 128-channel system were acquired at rest and analyzed through microstate analysis.
Results: We found a reduced presence of microstate B in BD patients compared to HC, since BD patients have a tendency to transit from the microstate B to the microstates C and D significantly more than HC. Furthermore, microstate B features were correlated with DES, state STAI and trait STAI scores.
Conclusion: The reduced presence of microstate B might be associated with episodic autobiographic memory deficit, exaggerated self-focusing and states of dissociations characteristic of BD. Strong correlations of microstate B metrics and dynamics with symptoms of dissociation and anxiety across the two groups supported this interpretation.
