A hybrid brain-computer interface based on motor intention and visual working memory

Non-invasive electroencephalography (EEG) based brain-computer interface (BCI) is able to provide alternative means for people with disabilities to communicate with and control over external assistive devices. A hybrid BCI is designed and developed for following two types of system (control and monitor). Our first goal is to create a signal decoding strategy that allows people with limited motor control to have more command over potential prosthetic devices. Eight healthy subjects were recruited to perform visual cues directed reaching tasks. Eye and motion artifacts were identified and removed to ensure that the subjects\u27 visual fixation to the target locations would have little or no impact on the final result. We applied a Fisher Linear Discriminate (FLD) analysis for single-trial classification of the EEG to decode the intended arm movement in the left, right, and forward directions (before the onsets of actual movements). The mean EEG signal amplitude near the PPC region 271-310 ms after visual stimulation was found to be the dominant feature for best classification results. A signal scaling factor developed was found to improve the classification accuracy from 60.11% to 93.91% in the two-class (left versus right) scenario. This result demonstrated great promises for BCI neuroprosthetics applications, as motor intention decoding can be served as a prelude to the classification of imagined motor movement to assist in motor disable rehabilitation, such as prosthetic limb or wheelchair control. The second goal is to develop the adaptive training for patients with low visual working memory (VWM) capacity to improve cognitive abilities and healthy individuals who seek to enhance their intellectual performance. VWM plays a critical role in preserving and processing information. It is associated with attention, perception and reasoning, and its capacity can be used as a predictor of cognitive abilities. Recent evidence has suggested that with training, one can enhance the VWM capacity and attention over time. Not only can these studies reveal the characteristics of VWM load and the influences of training, they may also provide effective rehabilitative means for patients with low VWM capacity. However, few studies have investigated VWM over a long period of time, beyond 5-weeks. In this study, a combined behavioral approach and EEG was used to investigate VWM load, gain, and transfer. The results reveal that VWM capacity is directly correlated to the reaction time and contralateral delay amplitude (CDA). The approximate magic number 4 was observed through the event-related potentials (ERPs) waveforms, where the average capacity is 2.8-item from 15 participants. In addition, the findings indicate that VWM capacity can be improved through adaptive training. Furthermore, after training exercises, participants from the training group are able to improve their performance accuracies dramatically compared to the control group. Adaptive training gains on non-trained tasks can also be observed at 12 weeks after training. Therefore, we conclude that all participants can benefit from training gains, and augmented VWM capacity can be sustained over a long period of time. Our results suggest that this form of training can significantly improve cognitive function and may be useful for enhancing the user performance on neuroprosthetics device

Controlling a Mouse Pointer with a Single-Channel EEG Sensor

Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointe

Development of a practical and mobile brain-computer communication device for profoundly paralyzed individuals

Thesis (Ph.D.)--Boston UniversityBrain-computer interface (BCI) technology has seen tremendous growth over the past several decades, with numerous groundbreaking research studies demonstrating technical viability (Sellers et al., 2010; Silvoni et al., 2011). Despite this progress, BCIs have remained primarily in controlled laboratory settings. This dissertation proffers a blueprint for translating research-grade BCI systems into real-world applications that are noninvasive and fully portable, and that employ intelligent user interfaces for communication. The proposed architecture is designed to be used by severely motor-impaired individuals, such as those with locked-in syndrome, while reducing the effort and cognitive load needed to communicate. Such a system requires the merging of two primary research fields: 1) electroencephalography (EEG)-based BCIs and 2) intelligent user interface design. The EEG-based BCI portion of this dissertation provides a history of the field, details of our software and hardware implementation, and results from an experimental study aimed at verifying the utility of a BCI based on the steady-state visual evoked potential (SSVEP), a robust brain response to visual stimulation at controlled frequencies. The visual stimulation, feature extraction, and classification algorithms for the BCI were specially designed to achieve successful real-time performance on a laptop computer. Also, the BCI was developed in Python, an open-source programming language that combines programming ease with effective handling of hardware and software requirements. The result of this work was The Unlock Project app software for BCI development. Using it, a four-choice SSVEP BCI setup was implemented and tested with five severely motor-impaired and fourteen control participants. The system showed a wide range of usability across participants, with classification rates ranging from 25-95%. The second portion of the dissertation discusses the viability of intelligent user interface design as a method for obtaining a more user-focused vocal output communication aid tailored to motor-impaired individuals. A proposed blueprint of this communication "app" was developed in this dissertation. It would make use of readily available laptop sensors to perform facial recognition, speech-to-text decoding, and geo-location. The ultimate goal is to couple sensor information with natural language processing to construct an intelligent user interface that shapes communication in a practical SSVEP-based BCI

Systems engineering approaches to safety in transport systems

openDuring driving, driver behavior monitoring may provide useful information to prevent road traffic accidents caused by driver distraction. It has been shown that 90% of road traffic accidents are due to human error and in 75% of these cases human error is the only cause. Car manufacturers have been interested in driver monitoring research for several years, aiming to enhance the general knowledge of driver behavior and to evaluate the functional state as it may drastically influence driving safety by distraction, fatigue, mental workload and attention. Fatigue and sleepiness at the wheel are well known risk factors for traffic accidents. The Human Factor (HF) plays a fundamental role in modern transport systems. Drivers and transport operators control a vehicle towards its destination in according to their own sense, physical condition, experience and ability, and safety strongly relies on the HF which has to take the right decisions. On the other hand, we are experiencing a gradual shift towards increasingly autonomous vehicles where HF still constitutes an important component, but may in fact become the "weakest link of the chain", requiring strong and effective training feedback. The studies that investigate the possibility to use biometrical or biophysical signals as data sources to evaluate the interaction between human brain activity and an electronic machine relate to the Human Machine Interface (HMI) framework. The HMI can acquire human signals to analyse the specific embedded structures and recognize the behavior of the subject during his/her interaction with the machine or with virtual interfaces as PCs or other communication systems. Based on my previous experience related to planning and monitoring of hazardous material transport, this work aims to create control models focused on driver behavior and changes of his/her physiological parameters. Three case studies have been considered using the interaction between an EEG system and external device, such as driving simulators or electronical components. A case study relates to the detection of the driver's behavior during a test driver. Another case study relates to the detection of driver's arm movements according to the data from the EEG during a driver test. The third case is the setting up of a Brain Computer Interface (BCI) model able to detect head movements in human participants by EEG signal and to control an electronic component according to the electrical brain activity due to head turning movements. Some videos showing the experimental results are available at https://www.youtube.com/channel/UCj55jjBwMTptBd2wcQMT2tg.openXXXIV CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - Ingegneria dei sistemiZero, Enric

自然視条件下脳波計測の精度向上を可能にする眼球運動情報を用いた解析方法に関する研究

As the technique of electroencephalogram (EEG) developed for such many years, its application spreads and permeates into different areas, such like, clinical diagnosis, brain-computer interface, mental state estimation, and so on. Recently, using EEG as a tool for estimate people’s mental state and its extensional applications have jump into the limelight. These practical applications are urgently needed because the lack of subjectively estimating methods for the so called metal states, such as the concentration during study, the cognitive workload in driving, the calmness under mental training and so on. On the other hand, the application of EEG signals under daily life conditions especially when eye movements are totally without any constrains under a ‘fully free-view’ condition are obedient to the traditional ocular artifact suppression methods and how it meets the neuroscience standard has not been clearly expounded. This cause the ambiguities of explaining the obtain data and lead to susceptive results from data analysis. In our research, based on the basic idea of employing and extending EEG as the main tool for the estimation to mental state for daily life use, we confirmed the direction sensitivity of ocular artifacts induced by various types of eye movements and showed the most sensitive areas to the influence from it by multi zone-of-view experiment with standard neuroscience-targeted EEG devices. Enlightened from the results, we extended heuristic result on the use of more practical portable EEG devices. Besides, for a more realistic solution of the EEG based mental state estimation which is supposed to be applied for daily life environment, we studied the signal processing techniques of artifact suppression on low density electrode EEG and showed the importance of taking direction/eye position information into account when ocular artifact removal/suppression. In summary, this thesis has helped pave the practical way of using EEG signals toward the general use in daily life which has irregular eye movement patterns. We also pointed out the view-direction sensitivity of ocular artifact which helps the future studies to overcome the difficulties imposed on EEG applications by the free-view EEG tasks.九州工業大学博士学位論文 学位記番号:生工博甲第262号 学位授与年月日:平成28年3月26日1 Introduction|2 EEG measurements and ocular artifacts|3 Regression based solutions to ocular artifact suppression or removal in EEG|4 Measuring EEG with eye-tracking system|5 Direction and viewing area-sensitive influence of EOG artifacts revealed in the EEG topographic pattern analysis|6 Performance improvement of artifact removal with ocular information|7 Summary九州工業大学平成27年

Psychologie und Gehirn 2007

Die Fachtagung "Psychologie und Gehirn" ist eine traditionelle Tagung aus dem Bereich psychophysiologischer Grundlagenforschung. 2007 fand diese Veranstaltung, die 33. Jahrestagung der „Deutschen Gesellschaft für Psychophysiologie und ihre Anwendungen (DGPA)“, in Dortmund unter der Schirmherrschaft des Instituts für Arbeitsphysiologie (IfADo) statt. Neben der Grundlagenforschung ist auch die Umsetzung in die Anwendung erklärtes Ziel der DGPA und dieser Tradition folgend wurden Beiträge aus vielen Bereichen moderner Neurowissenschaft (Elektrophysiologie, bildgebende Verfahren, Peripherphysiologie, Neuroendokrinologie, Verhaltensgenetik, u.a.) präsentiert und liegen hier in Kurzform vor

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Proceedings of the 3rd International Mobile Brain/Body Imaging Conference : Berlin, July 12th to July 14th 2018

The 3rd International Mobile Brain/Body Imaging (MoBI) conference in Berlin 2018 brought together researchers from various disciplines interested in understanding the human brain in its natural environment and during active behavior. MoBI is a new imaging modality, employing mobile brain imaging methods like the electroencephalogram (EEG) or near infrared spectroscopy (NIRS) synchronized to motion capture and other data streams to investigate brain activity while participants actively move in and interact with their environment. Mobile Brain / Body Imaging allows to investigate brain dynamics accompanying more natural cognitive and affective processes as it allows the human to interact with the environment without restriction regarding physical movement. Overcoming the movement restrictions of established imaging modalities like functional magnetic resonance tomography (MRI), MoBI can provide new insights into the human brain function in mobile participants. This imaging approach will lead to new insights into the brain functions underlying active behavior and the impact of behavior on brain dynamics and vice versa, it can be used for the development of more robust human-machine interfaces as well as state assessment in mobile humans.DFG, GR2627/10-1, 3rd International MoBI Conference 201