Brain–computer interfaces (BCIs) allow communication without movement. In a typical BCI, a user performs voluntary mental tasks that each produce distinct patterns of electrical activity in the electroencephalogram (EEG). Automated signal processing software tries to identify which mental tasks a user performed at specific times and thereby infer user intent. Most modern BCIs rely on one of three types of mental tasks, which are associated with different types of brain activity (Wolpaw et al., 2002):

Despite some delightfully strident arguments within the research community, there is no general agreement on which approach is best. Instead, different BCIs are better suited to different users, needs, environments, applications, and other parameters. The main drawback of the P300 BCI is that users must pay attention to specific events, usually flashes on a monitor. Hence, users must pace themselves according to the system, and may find the flashes annoying. On the other hand, P300 BCIs seem to work for nearly all healthy users, unlike (at least) ERD BCIs (Guger et al., 2003, 2009; Allison and Neuper, 2010). P300 BCIs require almost no training for the system or user, and have always been relatively fast; the first BCI to exceed 100 bits/min was a P300 BCI (Brunner et al., 2011). Recent work has highlighted many new options for improving the P300 BCI (e.g., Münßinger et al., 2010; Townsend et al., 2010; Frye et al., 2011; Kaufmann et al., 2011; Jin et al., 2012). Therefore, P300 BCIs should remain prominent for at least the near future.

Most P300 BCIs allow users to choose one target from several options, such as letters or numbers presented on a monitor (Farwell and Donchin, 1988; Townsend et al., 2010; Brunner et al., 2011). The user is asked to focus on one item, such as the letter “G,” and count each time it flashes while ignoring other events. Next, different groups of letters briefly flash, in sequence, until the system has presented a prespecified number of targets (Farwell and Donchin, 1988) or accurate classification is possible based on the EEG data (Jin et al., 2012). Since counting each flash produces a brainwave called the P300, which is not elicited by ignored flashes, the system can identify the target letter by identifying which flashes elicited a P300. The system presents this letter to the user, and then moves to the next target.

However, like most BCIs, P300 BCIs are hampered by the need for conductive gel to get a good contact between each electrode and the scalp. Preparing a subject for conventional EEG recording requires abrading the skin under each electrode, positioning each electrode over the abraded area, and squirting electrode gel underneath each electrode. Getting a good contact between each electrode and the scalp usually requires further skin abrasion and application of gel. The gel is uncomfortable to many subjects, and must be washed out of both the cap and hair later. This procedure greatly increases the time and inconvenience needed for any EEG recording session. Also, after a few hours of use, the gel may dry, and new gel must be applied (Ko and Hynecek, 1974).

These problems reduce the appeal of EEG-based technologies to most users, and can be especially pronounced for severely disabled users – even though these are the people who need BCIs most. Some conditions can increase skin sensitivity, making the skin abrasion process more painful. Since some of these patients cannot communicate without a BCI, they may have no way to convey their distress during preparation. Hence, working with gel-based electrodes increases dependence on friends, family, or other caretakers.

Numerous recent articles that survey different end users have further confirmed that dry electrodes are a very high priority. Casson et al. (2010) surveyed neurologists and found that almost 90% agreed there is a clinical need for “wearable electrodes.” Huggins et al. (2011) surveyed 61 ALS patients and found one of their main concerns was “set-up simplicity.” Zickler et al. (2011) surveyed severely disabled users and found that major issues included “possibility of independent use” and “easiness of use.” Blain-Moraes et al. (2012) presented a focus group study with eight ALS patients and nine carers. Two of their main concerns were a more convenient way to sense brain signals, and facilitating independence for both users and carers. The future BNCI roadmap, an extensive analytical effort developed through many established BCI stakeholders through 2010 and 2011, identified “practical electrodes” as one of the two disruptive technologies in BCI research.

Since dry electrodes can substantially simplify EEG recording, and are identified as important across numerous surveys, there should be a strong interest in them. Indeed, numerous articles over many years have explored dry electrodes to record EEG and other physiological signals (e.g., Roman, 1966; Richardson, 1967, 1968; Ko and Hynecek, 1974; Taheri et al., 1994; Gevins et al., 1995). However, early dry electrodes had various problems including reduced signal quality, robustness to movement, electrical artifacts, cost, and comfort.

There are two general approaches to dry electrodes, each with distinct problems (Portnoy et al., 1974; Tam and Webster, 1977; Gargiulo et al., 2008; Volosyak et al., 2010). One approach uses capacitive sensors, which do not require direct contact with the scalp. For such systems, motion artifact has been an insurmountable problem so far. The second approach relies on penetrating the first layer of the skin, such as through micro-needles, bristle sensors, or mechanical springs that press electrodes into the skin (Taheri et al., 1994; Matteucci et al., 2007; Popescu et al., 2007; Grozea et al., 2011). These systems may be uncomfortable. And, with any dry electrode, a paramount concern is validating signal quality for a specific application in real-world circumstances.

Recently, interest in dry electrodes has increased considerably. A PubMed search on February 1, 2012 for “dry electrode EEG” revealed 37 articles; only five were published before 2000, and another five were published before 2007. An otherwise identical search that replaced “EEG” with “BCI” found six articles, all published 2007 or later. Many companies are also producing dry electrode systems. Most sales are simple toy systems that detect drowsiness or fatigue with one electrode, unlike more complex multi-electrode systems that detect conventional BCI signals such as P300, SSVEP, and ERD.

Of the six journal publications that include “EEG” and “BCI,” there seems to be a dearth of P300 BCI work. Carabalona et al. (2009) focuses primarily on rehabilitation rather than dry electrodes. Popescu et al. (2007) only addressed motor imagery BCIs. Luo and Sullivan (2010) and Chi et al. (2011) only worked with SSVEP. Grozea et al. (2011) did explore P300s, but used an auditory paradigm, which is appealing to blind users but much less common and effective than a visual P300 approach. The article relied on only three electrodes: Cz and P1, referenced to Fz. This montage is not optimal for a visual P300 approach, which better works using components from parieto-occipital regions. Also, an Fz reference electrode is much more vulnerable to blink artifact than the more conventional mastoid or earlobe reference – and P300 BCIs are more vulnerable to eyeblink artifact than other BCIs. Although the article states that feedback was provided, it never mentions whether or how it was presented with the P300 BCI. Zander et al. (2011) explored ERPs and alpha activity. The ERP work used a completely novel display, unlike any P300 BCI yet. Their dry electrode system used only three electrodes, all located in a horizontal line. As with the Grozea et al. (2011) article, the additional spatial information available from a broader montage would have improved performance (e.g., Krusienski et al., 2006, 2008). Furthermore, Zander et al. (2011) was an offline study – subjects did not receive feedback, and classification accuracy was only estimated from offline data. Most importantly, their study showed that dry electrodes were statistically significantly worse (lower classification accuracy) than gel-based alternatives. Their classification accuracy was about 6% worse with the dry electrode system.

In addition to these journal publications, some conference papers have presented dry electrode systems. For example, our 2007 conference paper evaluated a dry electrode system in a gaming context, and a later conference paper used the same company’s system in a P300 copy-spelling task (Trejo et al., 2007; Sellers et al., 2009). Both of these studies used relatively few subjects, did not report comfort, have never been translated to a journal paper, and reported poor performance. For example, the latter article reported only about 70% accuracy with a P300 BCI copy-spelling task using row–column flashes with a 6 × 6 matrix.

All of these factors underscore the opportunity and need to explore well-established P300 methods with dry electrodes. The primary goal of this study was to assess a new dry electrode system in an otherwise conventional P300 BCI. A total of 23 subjects used a P300 BCI that was very much like other P300 BCIs in terms of the display, task, paradigm, signal processing, and other details. We present conventional analyses such as accuracy as well as subjective report regarding the comfort of the dry electrode system.

Before presenting results with the P300 speller, we first address the a priori question of whether the raw data from the two electrode types looked similar. Figure 3 presents 8 s of raw EEG data recorded from one subject with both gel and dry electrodes. The raw data look similar for both electrode types, including the noise created by eyeblink artifacts and some high frequency activity.

The next question is whether the ERPs look similar across both electrode types. Figure 3 shows the EP for dry and gel based electrodes at electrode position Cz for the training and copy-spelling run for one subject. The EP reaches its maximum of about 6 μV after about 240 ms in both cases. The EP looks very similar for the dry and gel based electrodes and the comparison of the training and copy-spelling run shows that the EP is very stable over time.

This is the first journal publication to show that dry electrodes can yield performance comparable to gel electrodes with a P300 BCI. This is an important outcome, given the promise and prominence of both dry electrodes and P300 BCIs. Subjects did not consider the dry electrode system uncomfortable, and it required no gel and reduced preparation time. Although the dry electrode system might be more prone to movement artifact and ambient electrostatic charges, these were not a problem in the present study. Overall, the new system is generally comparable to or better than gel electrodes in many ways.

This work is also important because it demonstrates that the dry electrode system can function in a relatively unconstrained field setting. Dry electrodes typically entail much higher impedance values than gel electrodes. This is the main reason why gel is so common; the gel provides contact between the scalp and each electrode that greatly reduces impedance. Otherwise, high impedance will impair signal quality and increase vulnerability to electrical artifact, such as from external devices or movement. The dry electrode system allows good performance despite high impedances by using multiple gold-coated pins in each electrode, as well as an integrated amplifier within each electrode.

Interestingly, although the dry electrode system resulted in EEG data, ERPs, and BCI accuracy comparable to gel electrodes, there were notable differences. The average peak P300 amplitude was lower in the present study. The dry electrodes showed higher signal drifts below 3 Hz than gel based electrodes. Neither of these difference had a notable effect on classification accuracy. However, these differences might be relevant for other types of BCIs. For example, BCIs that rely on slow cortical potential changes (Birbaumer et al., 1999) might be more sensitive to signal drift below 3 Hz.

Although this is a promising start, there are many issues that still need to be explored. As noted, dry electrodes need to be explored with a much wider variety of BCI systems, with different mental tasks, EEG signals, and other details. Comfort and other subjective factors should be assessed in other circumstances, such as with electrodes mounted in other headwear, tasks requiring physical movement, and long-term use. All of the subjects in this study, like all published dry electrode studies, were healthy. Dry electrodes should be validated with persons with severe disabilities, since they often need BCIs more than healthy users. With some such users, special concerns (such as fasciculations that produce uncontrolled movement) might be especially problematic for dry electrodes. We also recommend focusing on other comparisons between dry and gel electrodes. While accuracy and comfort are very important, research should also parametrically compare preparation time, reliance on outside support, and other factors. These may be more difficult to paradigmatically assess, but are critical factors in BCI adoption (Allison, 2010; Casson et al., 2010; Huggins et al., 2011; Zickler et al., 2011; Blain-Moraes et al., 2012) and are worth the extra trouble.

The Christoph Guger, Gunther Krausz, and Guenter Edlinger are full-time employees of g.tec medical engineering GmbH, Guger Technologies OG, and the Christoph Guger and Guenter Edlinger are its co-CEOs.