I'll try to guide your research a bit rather than giving you a direct answer. One-click recipes aren't too good for obtaining optimal results, and this requires some careful insight. After all, you are dealing with biological signals.
To develop a real-time filter you need to process each sample/window before the next one comes up. You need to take into account the CPU time available for your calculations (depending on the platform) and the number of signals you must filter at once (with the subsequent impact in used memory, of course). After all, a digital filter is no more than a bunch of sums and products.
Now you should study the kind of signal you need to filter. You seem to be handling frequencies from delta to beta waves. You need an insight in the characteristics of each signal so that you keep them after the filtering (e.g. if studying brain activity during sleep, you should make sure that you don't miss out k-complexes and spindles during Phase II). This way you can look into some values: the maximum band pass ripple you are allowed, the width of the transition band, etc.
Having this information, after some research (e.g. Digital filter design by Burrus & Parks; Discrete-time signal processing by Oppenheim & Schafer) you can choose the kind of filter you need, and go into further details about implementation.
Fortunately, with a simple google query you can find out lots of things. There are Matlab toolboxes available to aid your filter design:
FDATool, Matlab filter design toolbox.
you can also take a look on ERPLab, which seems a good starting point.
And lots of free papers: Google query for "EEG filtering".
The higher the order of the filter, the more accurate it is, but the more operations per second it needs. So the main constraint here is that you need to keep it real-time for a bunch of signals.