**4. Non-invasive BMI control of a robotic arm using an EEG-emulated demultiplexer**

EEG emulated demultiplexer is an emulation of a demultiplexer, a 1-to-n serial to parallel converter, a device that receives a serial input and distributes it to n outputs. An example of an EEG emulated 1-to-2 demultiplexer is shown in **Figure 12** [40]. It is driven by alpha rhythm.

**Figure 12.** *EEG emulated demultiplexer.*

**Figure 13.**

*The BMI task of controlling two motors by a single EEG channel using EEG demultiplexer.*

As **Figure 12** shows, a serial EEG signal is divided into two segments (frames). Those two EEG frames, A0 an A1, with encoded intent for commands to external devices, are sent to the EEG demultiplexer. Both frames can be defined as binary channels, but A0 can also be defined as multi-valued channel. The binary channels are used as binary addresses for the address decoder of the demultiplexer, while the multivalued channel is viewed as a data input of the demultiplexer. The EEG demultiplexer contains an address converter and a data converter. The signals enter the redundant 1-to-2 demultiplexer which sends the data channel to one of the two output channels c1 and c2, defined by the address decoder. Those output channels control two devices, for example two motors of a robotic arm. The demultiplexer used is redundant, because for addressing 2 output channels it uses 2 address lines, instead of just one. That has been done to increase the accuracy of EEG addressing (e.g., [41]).

The BMI task considered is shown in **Figure 13** [40].

As shown in **Figure 13**, a robotic arm should move from start region A to goal region B. The horizontal projection of the arm is such that if moved toward goal area B, it would hit an obstacle C along the way. In order to avoid the obstacle, the wrist of the arm should be moved up, so that horizontal projection of the arm is shortened before it reaches the obstacle C. The BMI task for the subject is: from a single EEG

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

channel, generate an EEG pattern that will move the robot arm from A to B, avoiding C. So, the task is to control two motors from a single channel EEG.

**Figure 14** shows the experimental setup [40].

As **Figure 14** shows, a subject is sitting in front of a robotic arm and sends EEG commands such that the task of moving the arm while avoiding and obstacle is achieved.

The experimental trial of an EEG demultiplexer controlling a robot is shown in **Figure 15** [40].

As **Figure 15** shows, a raw EEG is received by the BMI system and is shown in Channel 1. Channels 2 and 3 are not recorded. Channel 5 is the filtered EEG to obtain the alpha rhythm. Channel 4 is the filtered alpha rhythm to obtain a signal which represents the alpha rhythm envelope. That signal is tested against a threshold value, shown in the same channel. Channel 6 contains two frames, each showing

#### **Figure 14.**

*Experimental setup for a BMI based on a EEG demultiplexer.*

#### **Figure 15.** *The screen of an experimental trial of an EEG demultiplexer.*

a pulse for how long the duration in which the envelope is above the threshold. Also, in the **Figure 15** can be seen that the binary value of frames is A1A0 = 01.

The channel 6 is the EEG demultiplexer channel. First the binary values of the frames are computed, in this case A1A0 = 01. That is a command to send the data to the chosen motor. The data are computed from the duration of the signal in frame A0, and a signal to move is sent to the motor. The demultiplexer commands are defined as A1A0 = 00 do nothing, A1A0 = 1X, change motor, and A1A0 = 01 move motor. Thus, control of two motors using a single channel EEG is achieved.

**Table 3** [40]. shows an experiment of a BMI using EEG demultiplexer in solving the problem of moving a robotic arm from A to B avoiding an obstacle at point C along the way.

As can be seen from **Table 3**, the threshold value of the alpha band envelope is set to 25. At trial 1, the frame A1 has a value of 20 < 25, and frame A0 has a value 0 < 25. So the binary values of the input lines to the demultiplexer are a1a0 = 00. The output line of the demultiplexer is c1, which activates motor M0 which is for horizontal movement of the robot arm. The command a1a0 = 00 means "do nothing" and the robot arm stays and its initial position 127, which is in the start region A. In the second trial the subject generates EEG such that C1 = 23 < 25, and C0 = 36 > 25, so the input demultiplexer lines are a1a0 = 01. The currently addressed


#### **Table 3.**

*Experiment of a BMI using EEG demultiplexer to control a robotic arm to move from point a to point B avoiding an obstacle at point C along the way.*

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

**Figure 16.** *Achievement motivation space for experiments in BMI for controlling a robot arm using an EEG demultiplexer.*

motor M0 moves from position 127 to position 112. The subject drives the robot arm horizontally, up to position 39. The obstacle is at position 35, so the subject changes the movement to the motor M3 which will move the arm wrist vertically. It should be noted that the subject does not know the internal coordinates of the motors, and s/he only sees the movement in space, and s/he estimates how far the robotic arm is from the visible obstacle. In trial 8 the subject changes the alpha rhythm pattern co that A1 = 77 > 25 and A0 = 4 < 25, i.e., a1a0 = 10 which changes the demultiplexer output and chooses the motor M3 which is still in its initial position 127. In trial 9 s/he moves that motor to position 136. With careful BMI control, the subject succeeds to achieve the goal area in robot coordinates M0M3 = (25, 217), avoiding the obstacle at M0M3 = (< 34, <217). Any position of M3 < 217 would hit the obstacle at M0 < 34.

**Figure 16** [42] shows some results of the experiments of a BMI using EEG demultiplexer in controlling a robotic arm, as described above.

The experimental investigation carried out 53 BMI experiments. Successful were 42 of them. **Figure 16** shows example of 5 experiments. Here the goal region is marked with symbol and the avoidance region (obstacle) with symbol . The participants build behavioral trajectory through the achievement motivation space in order to reach the goal region while avoiding the obstacle. The coordinates are the internal robot coordinates, unknown to the participants. The participants use the view of the robotic arm to navigate the arm using their EEG.

As can be seen from **Figure 16**, the starting region of robot movement in each experiment is around the coordinate M0M3 = (127,127). Using BMI and controlling generated alpha rhythm in an EEG sentence, various trajectories are achieved toward the goal region M0M3 (>34, >217), avoiding the obstacle region M0M3 = (< 34, < 217).

### **5. EEG emulated modem**

An EEG emulated modem [40] is a process in which a sentence (message, command) is encoded in an EEG and is decoded at some receiving site, for example in a computer. **Figure 17** shows the concept.

**Figure 17.** *EEG emulated modem.*

As **Figure 17** shows, the EEG signal is viewed as an EEG encoded sentence, which contains words represented by EEG frames. The sentence is encoded as an EEG modulation. A modulation process usually contains a carrier signal which is a good harmonic signal, modulated by a message. The EEG carrier signal is a stochastic (or chaotic) signal [43], and it has some statistical properties, such as mean value and standard variation, among others. And it can be decoded given some information about the encoding process. For example, if it is known that the message is encoded in the alpha band, then first the alpha band can be filtered out, and the envelope can be obtained containing the message, as it was done in [40].

Here the concept of modulation is wider than the classical harmonic signal modulation. It can be any way of encoding a sentence in an EEG.

The EEG modem is an approach toward application of BCI with a low number of channels, when several devices should be controlled with a minimum number of EEG channels.
