The sources of EEG
Monitoring the activity of the brain using EEG is a bit like following a football game with microphones placed outside the stadium: you know when something is happening because you can hear the crowd cheering and singing, and you can approximate the location of an event based on the relative amplitudes and delays of signals recorded at different locations. However, it is very difficult to know exactly what is happening on the field, which player has the ball and the individual behaviour of each supporter. Similarly, EEG is not a very good tool to identify the brain structures that generate the recorded activity, and is unable to monitor the behaviour of individual neurons or even of small brain structures. For these reasons, people working with EEG usually mention the location on the scalp where they record a specific activity rather than mentioning the sources. Hypotheses about these sources can be made but are hard to verify using only EEG. An algorithm known as low resolution electromagnetic tomography (LORETA) can be used to reconstruct sources based on multichannel EEG recordings [Pascual-Marqui, 2009], but precise reconstruction requires a high number of electrodes (64+), which is why this method has not been used with our equipment.
Evoked, induced and spontaneous activity
Signals obtained through EEG recordings are a superimposition of many contributions from either the brain, the rest of the body or the environment (see section 2.4.3). While it is possible to reduce muscular artefacts due to movement and control the electric noise of the environment, the contribution of the brain usually contains a lot of activity unrelated to the task that is being assessed. Consequently, it is required to filter EEG signals to optimize the SNR or signal-to-background ratio. This filtering process is much easier if the measured brain response is time-locked to a stimulus (see section 3.2.1). Time locked responses are called evoked potentials (EPs) if they reflect the processing of the physical properties of a stimulus or event-related potentials (ERPs) if they are a consequence of a more cognitive analysis of the stimulus. Activity unrelated to any stimulation is referred to as spontaneous potentials.
As mentioned in the previous paragraph, the brain supports self-generated spontaneous activity even in the absence of sensory or motor inputs. This phenomenon requires the presence of oscillators to maintain a basal activity and sustain more complex behaviours that can arise in the presence of a perturbation. Measurements using implanted electrodes have shown that oscillations can be found in the brain at all frequencies between 0.02 Hz and 600 Hz, representing more than four orders of magnitude of temporal scale [Buzsaki, 2006]. However, only a limited number of these oscillators are active at the same time, and they can change rapidly. The consequence is that short-lived oscillatory patterns of various durations and frequencies are constantly created and destroyed by the brain’s internal dynamics. For this reason, decomposition of EEG data in the frequency domain often leads to interesting results by separating components originating from different brain oscillators. Unfortunately, EEG measurements are not able to grasp the full extent of these oscillatory patterns. High-frequency oscillations are filtered out by the tissues separating the brain from the EEG electrodes, and activity recorded over 100 Hz typically does not originate from the brain. On the other side of the frequency range, slow oscillating patterns at less than 0.5 Hz are often dismissed from EEG data to correct the baseline drift, especially in the BCI community, since accurate measurements of low frequencies require large temporal windows that are not compatible with real-time applications.
The high variability of EEG data
The brain is a complex dynamic system, and the processes generating EEG signals cannot, therefore, be described by linear equations. In addition, EEG signals are nonstationary, which means their statistical properties can change over time. Non-linearity and non-stationarity imply that the response to a given stimulation can be different from one trial to another, even during the same recording session. The variability observed between trials is even larger between individuals, not only because each brain is unique, but also because of the influence of the skull, the environment and the exact position and contact impedance of the electrodes. To circumvent this variability so that EEG signals can be compared between trials and individuals, normalization of the data or calibration of the algorithms is recommended. Normalization refers to any procedure applied to the data to standardize one or several of their statistical properties. For example, it is possible to remove the mean of a signal so that the sum of its time series becomes zero. Calibration refers to a slightly different technique in which the algorithmis adjusted to the subject or the experimental conditions.
The first approach to analyse EEG data is to observe the time series and look for specific patterns. This technique works fairly well for brain activity that results in large amplitude waveforms visible on single-trial recordings, without having to resort to averaging several segments of data. The most visible pattern is the ® rhythm, mentioned in a previous chapter (Figure 2.9), which appears when a subject closes his or her eyes.
However, most EEG activity has an amplitude lower than the electrical background and is hard to spot without some averaging or filtering techniques. This section (3.2) and the next ones (3.3 and 3.4) will introduce several methods to extract relevant information from raw EEG data.
Typical frequency bands in EEG analysis
EEG is commonly divided into several frequency bands, which have been associated with different brain states. Although each rhythm originally describes a specific pattern linked with a specific task or activity, the names of the frequency bands are used regardless of their sources. These bands can be defined as follows:
1. The ± rhythms, associated with the ± band (0.5-4 Hz), are the slowest oscillations that can be measured by EEG. They are observed mostly over the frontal lobe during sleep and mind wandering and can reach high amplitudes during deep sleep (200 ¹V peak to peak). They have also been observed posteriorly in children.
2. The µ rhythms, associated with the µ band (4-8 Hz), can be recorded during sleep, meditation, mind wandering and drowsiness. They have also been reported during active inhibition tasks.
3. The ® rhythm, associated with the ® band (8-12 Hz), originally refers to the strong oscillations observed over the occipital and parietal cortices when subjects close their eyes. It is linked with an idle state of the visual cortex. Activity in the ® band can also be recorded over the sensorimotor cortex when idle. This is called the ¹ rhythm.
4. The ¯ rhythms, associated with the ¯ band (12-25 Hz), cover a large span of frequencies associated with active thinking and high alertness as well as with anxiety and stress. ¯ waves in the 12-15 Hz band can also be observed in the sensorimotor cortex as part of the sensorimotor rhythm (SMR).
5. The ° rhythms, associated with the ° band (25-100 Hz), were originally considered to be mostly noise. However, they have recently been associated with high-level cognitive tasks, such as cross-modal sensory processing, awareness and consciousness.
Independent components analysis (ICA)
In section 126.96.36.199, PCA was introduced as a method to decompose a set of signals into uncorrelated components. ICA is another technique for the identification of mixed sources inmultivariate data that decomposes a dataset into independent components, with statistical independence being stronger than linear uncorrelation. However, the decomposition provided by ICA is not unique, and some differences with previously mentioned algorithms (PCA and JD) should be noted [Langlois et al., 2010]:
• ICA does not provide a ranking of the components. In other words, there is no better or worse components unless the user decides to rank them following his own criteria.
• The extracted components are invariant to the sign of the sources.
• Perfectly Gaussian sources cannot be separated by ICA because it tries to separate their non-Gaussianity.
• If the sources of the signals are not independent, ICA finds a space of maximum independence.
Table of contents :
Table of Contents
List of Figures
List of Tables
1.1 What are we trying to do?
1.2 Thesis overview
1.3 List of publications
2 Brain-Computer Interfaces: Connecting Brains withMachines
2.1 What is a brain-computer interface (BCI) ?
2.2 How does it work ?
2.3 Examples of EEG-based BCIs
3 Methods of the Neural Interface Engineer
3.1 The nature of EEG signals
3.2 Time-domain analysis
3.3 Frequency-domain analysis
3.4 Filtering EEG signals
3.5 Machine Learning
4 Models and Networks of Attention
4.1 A history of attention modelling
4.2 Integrative model of attention and executive control
4.3 Vocabulary of attention
4.4 Anatomy of attentional networks
4.5 Some neurophysiological effects of attention
5 Modelling of steady-state activity fromtransient potentials
5.2 Transient and steady-state visual evoked potentials
5.3 Materials and methods
6 Application of SSVEPModelling to Brain-Computer Interfaces
6.2 Materials and methods
6.4 Validation on a real BCI
7 Prediction of Attentional Load during a Continuous Task
7.2 Materials and methods
7.3 Subjective feedback
8 Conclusion and Perspectives
A Papers as first author
A.1 Transient brain activity explains the spectral content of steady-state visual evoked potentials
A.2 Detection of steady-state visual evoked potentials using simulated trains of transient evoked potentials.
A.3 Towards cognitive BCI: Neural correlates of sustained attention in a continuous performance task.
A.4 A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback
B Visuals of experiments
B.1 Stimulations for visual evoked potentials
B.2 Continuous performance task
B.3 Serial reading task
C Magnified time-frequency maps
C.1 Visual evoked potential, average on 10 subjects
C.2 Average VEP, subject 4
C.3 Average VEP, subject 6