Interpreting Brain Waves

Introduction

Our brains contain neurons that work each time we think, move, feel or remember something. That work is carried out by electrical signals that zip from neuron to neuron. The paths through which these signals move are insulated.However, some of the signals still manage to escape. Scientists can detect those signals using electrodes. There are many types of sensors that are used in BCI research; the most common are shown in Figure 1. Scientists can also interpret what these signals mean and use them to direct a device of some kind (Grabianowski, 2007).

A brain–computer interface (BCI), sometimes called a mind-machine interface (MMI), direct neural interface (DNI), synthetic telepathy interface (STI) or brain–machine interface (BMI), is defined as a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions(Brain–computer interface, 2014).

BCI is not restricted to humans. Several laboratories have managed to record signals from monkey and rat cerebral cortices to operate BCIs to produce movement. Monkeys were able to navigate computer cursors on screen and to command robotic arms to perform simple tasks simply by thinking about the task and seeing the visual feedback (Miguel et. al., 2001).

There are several applications that could make use of this data such as: Assistive technology, virtual reality (Leeb et al., 2007), game controlling (Scherer et al., 2007) and robotics (Plant, Ponnapalli, & Southall, 2007; Satti, Coyle, & Prasad, 2011). The authors are interested in understanding these waves to be able to use them as an input to other systems. Several studies have been made in this field with varying results.

The authors will focus on the features extraction and classification of the motor-imagery tasks. A motor based BCI translates the subject’s motor intention which is reflected in the EEG recorded from the scalp into a control signal.Motor imagery is the mental representation of the performance without doing the action and it is an internal stimulus which induces indirect experience. Motor imagery is known to induce muscle activity and brain activity similar to that induced when performing the actual activity(Kim et al., 2014).

Figure 1.

Different types of sensors most commonly used in BCI research

A: Electrodes are placed non-invasively on the scalp(electroencephalography (EEG)) B: Electrodes are placed on the surface of the brain (electrocorticography (ecog)) C: Electrodes are placed invasively within the brain (single-neuronrecordings) Wolpaw, 2006.

In this chapter the authors propose a method for classification of the EEG signals. This method consists of three steps:

• 1.

  Pre-Processing for eliminating unwanted noise.

• 2.

  Features Extraction using Empirical Mode Decomposition (EMD) (Huang et al., 1998).

• 3.

  Classification using a Support Vector Machine (SVM) (Osuna, Freund, & Girosi, 1997).

The method is applied to real human data and better results relative to the state of art algorithms are obtained.

In section two, the authors give related work and the available techniques used to solve the problem of feature extraction and recognition of EEG signals. Details of the proposed model are presented in section three. The data set used numerical experiments and the results are shown in section four, where the authorsalso show the results obtained by the selected method. Finally, the conclusions are driven and presented in section five.