Formación de la autoestima

Among other various definitions, BCIs can be categorised as cue-based (synchronous) or self-paced (asynchronous) systems. Figure 3.3 shows different

43

examples between Self-Paced BCIs (SP-BCIs) and Cue-Based BCIs (CB-BCIs). As can be observed, CB-BCI systems have to inform the user when to start and stop thinking the command to the machine. It means the user must follow the computer’s own timing commands [5]. The majority of the current stage of EEG-based BCI systems are CB-BCIs, where the analysis and classification of brain signals are locked to the machine’s predefined timing protocol [7]. The advantage of CB-BCIs is that they provide a better classification rate and an easier analysis than SP-BCIs as the machine knows the precise time location of relevant events by providing specific cues or triggers to the users. For example, very common cue-based approaches are the P300 and SSVEP BCI systems, which were discussed earlier. However, this approach forces the users to keep concentrating on the computer’s command (e.g., looking at blinking objects) which is a very unnatural interaction approach. In addition, CB-BCI systems always need an external stimulus from the computer so that the computer can make decisions for the users [8].

44

On the other hand, SP-BCIs analyse the user’s brain signals continuously without a specific computer-controlled stimulus [9]. The users control the BCI system by intentionally performing a specific mental/cognitive task at any point they want [7]. This design is more intuitive to the users. It enables them to control the system in a more natural way according to the user’s own timing and speed of communication. It increases the usability and flexibility of the BCI systems [10]. In order to expand the BCIs from the indoor laboratory settings into real-world applications, the machine dependent timing constraints have to be resolved. For this reason, the SP-BCI system is essential for the real-world use of BCIs in the near future.

However, there is still a great challenge for SP-BCIs. It is much more complicated to analyse them than CB-BCIs as they have a lack of knowledge about the precise time location of the user’s command. The user’s control intention and timing are usually unknown to the machine [5, 7]. Therefore, SP-BCIs should continuously analyse the ongoing brain activity and they should be able to distinguish between Intentional-Control (IC) and Non-Control (NC) states. An IC state describes the process where the intended brain activity is supposed to produce a BCI output, which is quite straightforward. However, the NC state could be any other state besides the IC state. It can be also called a non-specific state (i.e., idle, daydreaming, other mental activities or performing some other actions) [8]. Distinguishing between the IC and NC state is very important in order for SP-BCIs to reduce the false-positive rate. In order to solve this issue, onset detection methods can be introduced (it will be explained in the following section).

In the case of these timing analysis difficulties, the classification performance in SP-BCIs is usually poorer than in CB-BCIs. In addition, the performance assessment

45

method of SP-BCI systems is not standardised and therefore it may vary depending on the values provided by papers and experimental settings. The following section, recent studies of SP-BCIs will be discussed together with their applications, evaluation methods and performance results.

In [80], a speech related SP-BCI was tested with a functional Near Infrared Spectroscopy (fNIRS) for 5 male subjects. The experiment was carried out as an off- line scenario and there were three types of speech activity: a normal audible speech, a silent speech (moving the articulatory muscles but without sound production), and speech imagery. The experiment consisted of 10 sentences (around 66 characters), which were taken from a news broadcast. The subjects were asked to produce each speech mode followed by pause periods, which were regarded as the idle state [81]. SVM was used for classification. In this paper, precision and recall were used for the evaluation method. The average result of 5 subjects had a 74% accuracy, 61% true- positive rate, 16% false-positive rate, 84% true-negative rate and the precision and recall values were 0.73 and 0.61, respectively [80].

In [82], motor imagery was used for the self-paced system. There were three right-handed subjects who were asked to perform real movements (i.e., extending their right wrist, holding it sill for about 1-2 seconds and then relaxing) on their own pace without any cue from the system. However, they were asked to leave at least a 4 seconds interval between the tasks. They used an electromyogram (EMG) in order to identify correct onset muscle activities. For the feature extraction, the Thomson Multitaper method was used for the Power Spectral Density (PSD) and the Davis-Bouldin Index (DBI) was applied for the selection of the features. The Naïve Bayes classifier was used for classification. In this study, the performance was analysed with a True-False (TF) difference rate and an average time between the correctly detected onset and the real

46

movement onset. The TF rate was defined as: TF = (TP/E – FP/(E+FP)) * 100, where

TP: True- Positive, FP: False-Positive and E: total number of events. In this

experiment, the authors achieved a TF rate of 95%, 69% and 59% in 3 different subjects and the average time was 325 ms, 788 ms and 688 ms respectively [82].

In [8], a tetraplegic subject, who got injured in the spinal cord, was trained with the cue-based motor imagery system during a 4 months period. After that stage, for the self-paced system study, the authors used two electrodes; Cz (foot representation area) and Fz (ground electrode). A single logarithmic band power feature was applied and a simple threshold was used to distinguish between the imaginary thought of foot movement and the rest (non-control) state. The aim of this experiment was to move a virtual wheelchair to the target area and lay it still there for a couple of seconds. The authors evaluated the subject’s performance with the percentage of the accurate stop at the target place. They achieved an around 90% accuracy value. However, for most of the duration of the experiment, the time that the wheelchair stopped moving was too short (between 0.08 and 0.88 seconds), where it was supposed to last for at least a couple of seconds. Therefore, even though, the results reported quite a high performance, there was still some doubt concerning the actual classification accuracy between the active and non-control states.