**Abstract**

This paper reviews efforts in a new direction of the EEG research, the direction of EEG emulated control circuits. Those devices are used in brain computer interface (BCI) research. BCI was introduced 1973 as a challenge of using EEG signals to control objects external to the human body. In 1988 an EEG-emulated switch was used in a brain machine interface (BMI) for control of a mobile robot. The same year a closed loop CNV paradigm was used in a BMI to control a buzzer. In 2005 a CNV flip-flop was introduced which opened the direction of EEG-emulated control circuits. The CNV flip-flop was used for BMI control of a robotic arm in 2009, and for control of two robotic arms in 2011. In 2015 an EEG demultiplexer was introduced. The EEG emulated demultiplexer demonstrated control of a robotic arm to avoid an obstacle. The concept of an EEG emulated modem was also introduced. This review is a contribution toward investigation in this new direction of EEG research.

**Keywords:** electroencephalography, EEG-emulated control circuits, brain-machine interface, robotic arm, tower of Hanoi, achievement motivation

### **1. Introduction**

In 1929 Berger carried out research on human electroencephalogram (EEG) and introduced EEG rhythms [1]. In 1973 Vidal [2] introduced the term Brain– Computer Interface (BCI), and he set a challenge of controlling objects external to the human body by using the signals from a human electroencephalogram (EEG). He actually stated two challenges for EEG researches:


Vidal [2] advised the use of various EEG signals, including EEG rhythms and event related potentials. Specifically, he challenged the use of the Contingent Negative Variation (CNV) event related potential.

Response to the BCI challenge was relatively slow in the years after 1973. The first report on control of an object using EEG was given by Vidal himself in 1977 [3]. He designed a responsive BCI, with active movement of eyes to elicit various visual evoked potentials (VEPs), in order to control a 2D movement of a cursor-like object on a screen. In 1988, three reports appeared related to control of objects using EEG

signals. Farwell and Donchin [4] used P300 event related potential to choose a letter from a computer screen and write text on that screen. Bozinovska et al. [5–8] used contingent negative variation (CNV) event related potential to control a buzzer. That work also responded to the second Vidal's challenge, as it introduced an adaptive filter to extract a time varying CNV potential. That work introduced a new taxonomy of brain potentials [8] which classified CNV as an anticipatory brain potential. Bozinovski et al. in 1988 [9–12] used changes in EEG alpha frequency band (contingent alpha variation, CαV) to control a movement of a physical object, namely a robot; that BMI solved the long lasting challenge of telekinesis (movement of a physical object with energy emanating from a human brain). And, after the 1973 BCI challenge, it was the first intention driven (rather than response driven) BCI. Those five pioneering BCI efforts appeared before 1990. Examples of works of other authors after 1990 related to this work are [13–16].

An EEG based BCI setup consists of the following steps: (1) Produce a state in a (human or animal) brain which will be manifested by a particular EEG signal in which a control command is encoded. (2) Record the EEG signal and transmit it to a computer. (3) Analyze the EEG signal and decode the encoded command. (4) Send the decoded signal to a controlled object, such as a visual object, or a sound object, or a physical object with a mass.

EEG is a classical modality of obtaining a brain signal, but other ways of recording brain signals (e.g. magnetoencephalogram) are also being developed. This paper deals only with the EEG modality used in a BCI.

There are two ways of generating EEG-encoded commands to control an object.


Another term used, besides BCI (brain computer interface) and BRI (brainresponse interface), is BMI (brain machine interface). It is usually used for control

#### *EEG-Emulated Control Circuits for Brain-Machine Interface DOI: http://dx.doi.org/10.5772/intechopen.94373*

of an object outside of the computer screen, for example a physical object with a mass, such as a robot. With that in mind, the term BMI is used in this paper.

A BCI can be carried out invasively and non-invasively. Invasive BCI records a signal inside a brain, which requires a surgical intervention. Non-invasive BCI records EEG from the scalp, which is outside the brain. For example, the first noninvasive BMI was carried out in 1988 [9–10] for control of a (mobile) robot, and the first invasive BMI for control a robot (arm) was carried out in 1999 [18]. It is worth mentioning that those two works were the only ones in the 20th century dealing with BMI for moving object with a mass.

An essential objective of the BCI software is to find an EEG feature which can be used as a switch for controlling an object. In addition to an EEG emulated switch, recently other EEG emulated control structures are being explored, such as a flipflop, demultiplexer, and modem. This paper will be devoted to that research.

Reviews of the BMI efforts (e.g., [19–21]) are present in the literature. Various robotic devices are being built (e.g, [22]). Many companies are involved in BMI (e.g. Emotiv [23], Kinova [24]).

In the sequel, we will first review the EEG emulated switch for control of a mobile robot. Then we will describe the EEG emulated flip-flop with applications of controlling robotic arms. Then we will describe an EEG demultiplexer and the EEG modem. Some results of current experimental research work using EEG demultiplexer are also shown.
