D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification methods to be accessible for a wide range of users. Currently, Electroencephalography (EEG) is the most widely used technology. Following the human non-invasive brain-actuated robot control demonstrated in 2004 (Millán et al, 2004), research has addressed control of other rehabilitation devices such as wheelchairs, robotic arms, small-size humanoids, and even teleoperation robots for telepresence applications (some of them developed by members of the Consortium). The majority of research in brain computer interfaces for robot control use non-invasive methods to record brain activity, and have explored the way that humans can deliver control orders to the machine using brain waves. For example, wheelchairs (Iturrate et al, 2009; Luth et al, 2007;, Rebsamen et al, 2007) were driven by using a BCI that detects the using steady-state potentials or P300 evoked potentials, or with detection of mental tasks (Vanacker et al, 2007). Other results focused on the motion of a robotic arm in two dimensions using motor imagery (Mc Farland & Wolpaw, 2008), controlling the opening and closing of a hand orthosis (Pfurtscheller et al, 2000) with sensory motor rhythms, using motor imagery to move a neuroprosthesis (Muller-Putz et al, 2005), using P300 potentials to control a humanoid robot (Bell et al, 2008), and the teleoperation of a mobile robot remotely located to develop navigation and exploration also with P300 potentials (Escolano, 2009). However, none of these projects has explored the type of human cognitive information that could be extracted in this process and how it could be used at all the autonomy levels of the robotic system. Technological Gaps in Merging Non-Invasive BCI and Robotics. Nevertheless, we are still far from any successful deployment of non-invasive brain-controlled devices, which is due to the fact that the already mentioned devices share common shortcomings that require substantial scientific advances: 1. The mental protocol for robot control is not natural for the user, i.e., the user’s intention is not explicit in the control. For example, in one of the wheelchairs, the user had to concentrate on rotating a 3D figure to turn right or on complex arithmetic operations to turn left. 2. There is no mutual self-adaptation between the human and the controlled device. Usually, adaptation is considered only at BCI level to take into account variability in brain activity across subjects and time, but there is not a mutual adaptation of the human to the robot and vice versa. 3. There is a lack of general and modular software architecture that successfully integrates the different BCI technologies, and that also complies with the requirements of hardware and software robotic architectures. This is important for large scale or integration projects. 4. The working scenarios are very simple controlled situations given the current state-of-the-art in autonomous robotics. For instance, the most complex tasks achieved are to open or close a robotic hand or robot navigation in a two dimensional world. Innovation in CORBYS In robotic related rehabilitation programs and in many other robot contexts it has been suggested that human cognitive processes, such as motor intention, attention, and higher level motivational states are important factors with potential to build a natural and increased cognitive interaction with the robot (Tee et al, 2008). The possible innovations of CORBYS are to build a BCI to decode and detect motor intentions in real-time and a rich cognitive information to be used in the subsequent levels of the robot hierarchy. This general objective needs innovation in several areas of brain computer interfacing, such as the development of signal processing and machine learning techniques in order to detect in real-time neural processes preceding movement and of cognitive processes related to the human execution of the task. In addition to this, a brain computer software architecture will have to be developed as an integration tool for the BCI system with interconnections with all the modules of the project. The previous aspect, plus other more related to the 148
D2.1 Requirements and Specification deployment of the application (human locomotion rehabilitation) will be addressed in the next sections. 15.2 Hardware for Noninvasive BrainComputer Interfaces Neural activity produces not only electrical activity but also other types such as magnetic and metabolic changes that can also be measured non-invasively. However, this type of measurement requires magnetoencefalography MEG (for the magnetic fields); and positron emission tomography PET, functional magnetic resonance fMRI and optical imaging (for the metabolic activity). These measurement techniques require sophisticated devices and facilities out of scope of a technology focused on a wide range of people. In addition to this, these methods are still technically demanding and costly, and most of them also have low temporal resolution which is a strong limitation for rapid communication (i.e. information transfer rate) being one of the most important features of communication devices. Focusing on the electrical brain activity, the electroencephalogram (EEG) is a sensory system that places several sensors in the surface of the scalp. These sensors allow the scalp recordings of neural activity in the brain by measuring the potential changes over time between a signal electrode and a reference one. An extra third electrode (ground) is used for getting differential voltage. Minimal EEG configuration consist of one active electrode, one reference electrode and one ground electrode (Teplan, 2002). These measurements are amplified and digitalised, and subsequently transferred to the computer. The EEG is the most accepted brain recording technique for brain-computer interfacing, since it is relatively cheap, portable, easy to use and has a low set-up cost. For these reasons, in line with the BCI community, the EEG is most recommended recording technology for CORBYS. However, there are two main dimensions that affect the selection of the type of EEG technology: the sensor technology and the sensor acquisition system. On the one hand, the sensors are the bottleneck of today non invasive BCI since they need long time preparation due to the use of conductive gel. Standard gel based EEG electrodes are passive electrodes, which require the skin to be cleaned, brushed, scraped in order to reduce their impedance and achieve sufficient signal quality. This abrasive skin treatment makes the subject uncomfortable and, when measured very repeatedly, exposed them to the risk of irritation, pain and infection (Teplan, 2002). Moreover, the use of electrolytic gel may cause electrical short between two electrodes in close proximity when the montage is not correctly carried out. To reduce time preparation, the active electrodes were developed which reduce artefacts and signal noise resulting from high impedance between the electrode(s) and the skin. The system setup becomes faster but the conductive gel is still used. Due to the previous reasons these wet sensors are not suitable for daily use in normal living environments, and in the last few years there has been an increasing interest in developing dry electrodes (they eliminate the need of conductive gel). These dry electrodes are based on a completely different sensory technology. The main features of both, wet and dry electrodes are listed below: 1. Contact impedance: wet electrodes showed a value that is the half of the dry one (Searle & Kirkup, 2010). 2. Static interference: the influence of an electrically charged object moving near recording electrodes (nonstationary electric fields) was analysed. Dry electrodes were tested in unshielded and shielded conditions, showing in both case smaller interference levels than wet types. In particular, dry electrodes, when shielding was employed, showed an interference level 40 dB lower than that experienced by wet electrodes (Searle & Kirkup, 2010). 3. Motion artefact: change in potential at the electrode/skin interface, due to skin movement, caused unwanted signals. Movement tests were conducted showing for dry electrodes an artefact value 20 dB higher than that for wet types at the commencement of the trials. Whereas at the end of the trial period, 149
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CORBYS Cognitive Control Framework
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D2.1 Requirements and Specification
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