To retrieve meaningful insights from these measurements, EEG brain imag- ing relies on detailed knowledge of the morphology of the subject head volume, which determines the scalp voltage distribution due to brain activ- ity. This is obtained from numerical models of the electric field propagation in the head, whose computation is very time-consuming and computation- ally intensive (EEG forward problem [94]).
EEG is a technique that can provide high temporal resolution as the de- tectable neural activity is concentrated at low frequencies, usually below 30 Hz. Typical EEG waveforms are localized at specific frequencies, usually re- ferred to as rhythms [95] and are indicative of the patients state (deep/light sleep, awake etc.). These rhythms are usually classified as follows:
• Delta rhythm: 0.5-4 Hz; • Theta rhythm: 4-8 Hz; • Alpha rhythm: 8-13 Hz; • Beta rhythm: 13-30 Hz; • Gamma rhythm: 30-100 Hz.
An example of brain rhythms is shown in Fig. 7.2.
Classical EEG analysis is based on the empiric examination of electroen- cephalograms and the spectral analysis of the basic rhythms, which finds application especially in subject monitoring, as in diagnosis of epilepsy; for example Fig. 7.2shows as an epileptic foci which causes a characteristic periodic waveform at 3 Hz.
EEG is also used to measure event-related potentials where brain waves are triggered by an external stimulus which could be visual, auditory and somatosensory and find more field of applications in pre-surgical treatment,
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Figure 7.2: EEG rhythms. All EEG brain rhythms are at frequency less than 30 Hz. Periodic wave at the bottom is generated by an epileptic foci (source: [96]).
neurofeedback and BCI.
In the pre-surgical treatment of epilepsy it has been proven as a high resolu- tion EEG source imaging is a valuable non-invasive functional neuroimaging technique [97].
The speed, ease, flexibility and low costs of this technique warrant its use in clinical practice. EEG is also used in neurofeedback application, where brain activation maps of the patient are computed and shown to him in real time. This creates a direct interaction between the subject and his neural activity, allowing him to try to modify his cerebral activity.
Advantages of EEG-based neurofeedback training have been proven by some studies as in the cases of severe palsies [98], in treating psychological disorders such as attention deficit/hyperactivity disorder (ADHD) [99], neu- rological disorders [100] as well as in the improvement and the influencing of improve cognitive performances in healthy subjects [101][102].
Interface (BCI) systems [103] and also consumer-oriented applications rang- ing from home care to neurofeedback and gaming controllers.
7.2.1 BCI systems
As introduced in Sec. 1.3, BCI is a communication system interfacing the human brain to external devices, like computers or actuators (see Fig.7.3). User commands are formed by recognizing brain activity with EEG and
Figure 7.3: BCI neurofeedback (source: [103]).
voltages measured by electrodes are sent to a computer. Data are interpreted to compute actuators commands and the feedback is closed by the subject’s perception of actuator actions or movements. Signal processing and actua- tor actions has been performed by software toolboxes and depends on the objective of the experiment.
Several real-time and open-source software platforms which allow design- ers and scientists to setup and execute BCI experiments in real and virtual environments have been introduced in the past few years. Examples of these platforms are BCI2000 [104], OpenVIBE [105] and BioSig [106].
BCI2000[104] is a general purpose platform capable of potentially incorpo- rate any brain signals coming from a set of electrodes on the scalp, process
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these signals to extract specific features that reflect the user’s intent (e.g. amplitude of evoked potentials) and then translate them into commands that operate a device (e.g. word processing program).
OpenVIBE [105] is an open source software platform which consists of a set of software modules that can be integrated to design BCI for both real and virtual reality applications. It proposes a user-friendly graphical language that allows non programmer to design a BCI experiment without writing any code.
BioSig[106] is a toolbox which offers several modules and algorithms for signal processing and real-time BCI.
An alternative to these BCI-oriented tools is represented by general pur- pose tools, such as SIMULINK (MathWorks, Inc. Natick, MA) which is a graphical programming environment widely used to design, simulate and auto code software for different scientific fields. A Simulink model is a hierarchical representation of the design of a system using a set of intercon- nected blocks. In this context it can be used to acquire, process and extract signal features. In addition, it allows the user to easily transfer data into the MATLAB environment for a more accurate post processing using for example the open source toolbox BioSig [106].
Connected to one of the commercial EEG systems, all of these BCI software tools allow one to analyze neurophysiological signals in real time, or to develop applications capable of providing practical assistance for patient diagnosis, treatment, and rehabilitation [107,108].
Despite of these useful applications, the dissemination of BCI systems is limited due to the drawbacks of EEG systems. The majority of these are high-expensive [17], thus not affordable even for research centres and uni- versities. Also a few low-cost systems in the range of $500-$1000 (USD) are supported by a large part of BCI software tools. However these systems are usually equipped with a small number of channels, have moderate to high noise and users cannot modify the position of the electrodes on the scalp. The main issue of these systems is the lack of a direct connection between BCI software tools and hardware implementation. There are no standard
libraries that allow one to connect them to the previously described BCI software tools. Hence this lack makes it difficult to use these hardware outside of the laboratory in which they were designed.
In addition, without stable software libraries which provide a way to con- nect the system to a BCI software tool, it is difficult to assess how the system functions and indeed whether it functions at all. If the system does not perform as expected it can be complicated to determine if the fault is due to a hardware or software issue and the time spent in trying to identify the source of error and correcting it can be substantial.
7.2.2 Creamino
In the scenario described in the previous section, the ARCES research team developed an Arduino-based cost-effective EEG system called Creamino, which has a fabrication cost of about 50 euros for the first 8 channels (work- ing system, including wet-contact active electrodes) and 30 euros per each additional 8 channels. These numbers are particularly attractive for systems designed to be used outside clinical environments, such as in home care or research-oriented applications. In addition a set of libraries which allows the system to be used in a variety of software environments has been devel- oped.
This work was developed in the scope of the European Project named CREAM. The project is focused on the multidisciplinary study of the neural substrates of creativity in different knowledge domains.
Creamino consists of a hybrid hardware/software platform capable of quickly linking the analog front-end (AFE) circuits for biopotential measurements and the PC used to acquire, visualize and process the EEG signal. Creamino allows one to reduce the time and the effort required to complete a new design thus leading to a rapid prototyping of an EEG-based BCI system. Specifically the contribution of my research team can be summarized as follows:
• A sample design of a microcontroller system supporting the connec- tion with the AFE, extract the EEG signals and transfer them to a PC;