D2.1 Requirements and Specification - CORBYS

Recommendations

Info

D2.1 Requirements and Specification As mentioned before, the artefacts may change the characteristics of EEG signal, limiting the accurate evaluation of the running brain processes, and be even incorrectly used as the source control in BCI systems. This is the reason why several automatic methods for artefact processing have been developed in the BCI literature. On the one hand there is the artefact Rejection to be taken into account, which is based on the rejection of contaminated trial, implicating loss of valuable data (Ramoser et al, 2000; Millán et al, 2002). Due to the very large number of undesirable signal in BCI systems, not all the contaminated trials can be rejected. Usually the most artefact affected epochs are excluded from the analysis. Therefore, the “cleaned” data are not completely free of artefacts. This methodology is only usable for offline analysis. In online real-time applications of a BCI system it is not possible to have time periods when the artefact contaminated signals are rejected, and as a consequence the BCI system cannot be used to control the device. On the other hand, there is the artefact removal, where the objective of these techniques is to remove the artefacts as much as possible while keeping the related neurological phenomenon intact. There are several types of artefact removal techniques: 1. Linear filtering: used to remove artefacts located in frequency bands that are not useful for the application of interest (Barlow, 1984; Ives & Schomer, 1988). Low-pass and high-pass filtering can be used to remove EMG and EOG artefacts respectively. The main advantage of this method is its simplicity, however this fails when neurological phenomena and the artefacts overlap or lie in the same frequency (Geetha & Geethalakshmi, 2011). 2. Linear combination and regression: a common technique for removing ocular artefacts from EEG signals (Croft & Barry, 2000), it uses a linear combination of the EOG contaminated EEG signal and the EOG signal. One problem of this approach is that subtracting EOG signal from the EEG one may also remove part of it. Regression techniques can be used to remove head-movement, jaw clenching and saliva swallowing artefacts (Geetha & Geethalakshmi, 2011). 3. Principal component analysis: strictly related to the mathematical technique of singular value decomposition (SVD), it requires uncorrelation between the artefacts and the EEG signal. This method has been reported to be not completely efficient with EOG, EMG and ECG artefacts, especially when they have comparable amplitude to the EEG signal (Lagerlund et al, 1997; Geetha & Geethalakshmi, 2011). 4. Blind source separation: a technique generally based on a wide class of unsupervised learning algorithms, it identifies the components that are attributed to artefacts and reconstruct the EEG signal without their contribution. Independent component analysis (ICA) is the most utilised (Choi et al, 2005). This method has been widely used to remove ocular artefacts, and also EMG and ECG artefacts in clinical studies. Its main advantage is that it does not rely on the availability of reference artefacts, however it usually needs prior visual inspection to identify artefact components (Geetha & Geethalakshmi, 2011). 5. Others: wavelet transform (Browne & Cutmore, 2002), nonlinear adaptive filtering (He et al, 2004) and source dipole analysis (SDA) (Berg & Scherg, 1994), even if they have so far a limited application in BCI systems. 156
D2.1 Requirements and Specification Technological Gaps in Artefacting for Non-Invasive BCI and Robotics: Historically, EEG signals have been considered noise prone, and even if human cognition often occurs during dynamic motor action most EEG studies examine subjects in static conditions (e.g. seated). However, the CORBYS project requires the patient to be walking during the use of the BCI system. During locomotion a large number of mechanical artefacts arise, which are associated for instance with head movements and can have amplitudes that are one order of magnitude greater than the corresponding brain related EEG signal. These kinds of artefacts are nonstationary, since the kinematics and kinetics of human walking exhibit both short-term (step to step) and longterm (over many steps) variability (Gwin et al, 2010). Very few studies have been realised on EEG during human locomotion. These include recording brain activity during pedalling a stationary bicycle (Jain, 2009), and during walking and running on a treadmill (Gwin et al, 2010). In the latter, the EEG signals of eight healthy volunteers were recorded from 248 active electrodes during a visual oddball discrimination task while simultaneously they walked and ran on a treadmill. A two-stage approach was used to remove locomotion artefacts: first a channel-based template regression procedure was applied, and then an Infomax independent component analysis (ICA) followed by a component-based template regression. Results showed that during walking condition locomotion the artefacts slightly contaminate the EEG signals in an event-related paradigm. However, during running conditions, the EEG signals are strongly affected by movement artefacts. The artefact removal technique implemented in the study was successfully applied separating brain EEG signals from gait related noise. This is believed to be the first study of EEG and event-related potentials from a cognitive task recorded during human locomotion. Its results show the feasibility of removing gait related movement artefact from EEG signals. However, notice that the type of EEG signals that this method separates are event-related responses, which are of different nature that the process that the CORBYS project will address (spontaneous activity). As is well-known in the BCI community, the EEG event-related responses are much more robust features (since they are more stationary) that the ones of the spontaneous EEG to be detected and classified. Then, it is expected that the impact of the EEG artefacts during locomotion will be much stronger in the CORBYS context than in the previous study. Required innovation in CORBYS The innovation that CORBYS will require is to develop signal processing techniques to address and correctly filter EEG artefacts acquired during human locomotion. Notice that these techniques will have to accommodate the non-stationary nature of the spontaneous process that will be used in CORBYS. In addition to this, these techniques for movement artefacts removal will need to support online analysis with a low calibration time, they will need to work with a low number of electrodes (maximum number of 16), and will need to require low computational resources. This innovation step will be challenging and crucial for the deployment of the BCI in the project, since the complete performance of the BCI will strongly depend on the precision and robustness achieved. 15.5 Decoding the Cognitive Process Required in CORBYS As mentioned in the introduction of this document, the general objective of CORBYS 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 (such as the feedback potentials and the attention) related to the human execution of the task. 15.5.1 EEG Decoding of Motor Intentions Several studies have demonstrated the appearance of EEG activity preceding human voluntary movement. 157
Page 1 and 2:
CORBYS Cognitive Control Framework
Page 3 and 4:
D2.1 Requirements and Specification
Page 5 and 6:
Page 7 and 8:
Page 9 and 10:
Page 11 and 12:
Page 13 and 14:
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Page 23 and 24:
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116: D2.1 Requirements and Specification
Page 165: D2.1 Requirements and Specification
Page 209: D2.1 Requirements and Specification
show all

D2.1 Requirements and Specification - CORBYS

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?